| 2025-05-15 | Space-Time Multigrid Methods Suitable for Topology Optimisation of Transient Heat Conduction | Magnus Appel, Joe Alexandersen et.al. | 2505.10168 | | 30 pages, 13 figures | Abstract (click to expand)This paper presents Space-Time MultiGrid (STMG) methods which are suitable for performing topology optimisation of transient heat conduction problems. The proposed methods use a pointwise smoother and uniform Cartesian space-time meshes. For problems with high contrast in the diffusivity, it was found that it is beneficial to define a coarsening strategy based on the geometric mean of the minimum and maximum diffusivity. However, other coarsening strategies may be better for other smoothers. Several methods of discretising the coarse levels were tested. Of these, it was best to use a method which averages the thermal resistivities on the finer levels. However, this was likely a consequence of the fact that only one spatial dimension was considered for the test problems. A second coarsening strategy was proposed which ensures spatial resolution on the coarse grids. Mixed results were found for this strategy. The proposed STMG methods were used as a solver for a one-dimensional topology optimisation problem. In this context, the adjoint problem was also solved using the STMG methods. The STMG methods were sufficiently robust for this application, since they converged during every optimisation cycle. It was found that the STMG methods also work for the adjoint problem when the prolongation operator only sends information forwards in time, even although the direction of time for the adjoint problem is backwards. |
| 2025-05-15 | Knowledge-Based Aerospace Engineering -- A Systematic Literature Review | Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén et.al. | 2505.10142 | | 40 pages, 14 figures, submitted to Advanced Engineering Informatics | Abstract (click to expand)The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation. |
| 2025-05-15 | Physical regularized Hierarchical Generative Model for Metallic Glass Structural Generation and Energy Prediction | Qiyuan Chen, Ajay Annamareddy, Ying-Fei Li et.al. | 2505.09977 | | | Abstract (click to expand)Disordered materials such as glasses, unlike crystals, lack long range atomic order and have no periodic unit cells, yielding a high dimensional configuration space with widely varying properties. The complexity not only increases computational costs for atomistic simulations but also makes it difficult for generative AI models to deliver accurate property predictions and realistic structure generation. In this work, we introduce GlassVAE, a hierarchical graph variational autoencoder that uses graph representations to learn compact, rotation, translation, and permutation invariant embeddings of atomic configurations. The resulting structured latent space not only enables efficient generation of novel, physically plausible structures but also supports exploration of the glass energy landscape. To enforce structural realism and physical fidelity, we augment GlassVAE with two physics informed regularizers, a radial distribution function (RDF) loss that captures characteristic short and medium range ordering and an energy regression loss that reflects the broad configurational energetics. Both theoretical analysis and experimental results highlight the critical impact of these regularizers. By encoding high dimensional atomistic data into a compact latent vector and decoding it into structures with accurate energy predictions, GlassVAE provides a fast, physics aware path for modeling and designing disordered materials. |
| 2025-05-15 | Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture | Linwei Zhang, LuFeng, Ruijia Liang et.al. | 2505.09907 | | | Abstract (click to expand)With the growing demand for healthy foods, agricultural product price forecasting has become increasingly important. Hass avocados, as a high-value crop, exhibit complex price fluctuations influenced by factors such as seasonality, region, and weather. Traditional prediction models often struggle with highly nonlinear and dynamic data. To address this, we propose a hybrid deep learning model, TCN-MLP-Attention Architecture, combining Temporal Convolutional Networks (TCN) for sequential feature extraction, Multi-Layer Perceptrons (MLP) for nonlinear interactions, and an Attention mechanism for dynamic feature weighting. The dataset used covers over 50,000 records of Hass avocado sales across the U.S. from 2015 to 2018, including variables such as sales volume, average price, time, region, weather, and variety type, collected from point-of-sale systems and the Hass Avocado Board. After systematic preprocessing, including missing value imputation and feature normalization, the proposed model was trained and evaluated. Experimental results demonstrate that the TCN-MLP-Attention model achieves excellent predictive performance, with an RMSE of 1.23 and an MSE of 1.51, outperforming traditional methods. This research provides a scalable and effective approach for time series forecasting in agricultural markets and offers valuable insights for intelligent supply chain management and price strategy optimization. |
| 2025-05-15 | Promise of Data-Driven Modeling and Decision Support for Precision Oncology and Theranostics | Binesh Sadanandan, Vahid Behzadan et.al. | 2505.09899 | | | Abstract (click to expand)Cancer remains a leading cause of death worldwide, necessitating personalized treatment approaches to improve outcomes. Theranostics, combining molecular-level imaging with targeted therapy, offers potential for precision oncology but requires optimized, patient-specific care plans. This paper investigates state-of-the-art data-driven decision support applications with a reinforcement learning focus in precision oncology. We review current applications, training environments, state-space representation, performance evaluation criteria, and measurement of risk and reward, highlighting key challenges. We propose a framework integrating data-driven modeling with reinforcement learning-based decision support to optimize radiopharmaceutical therapy dosing, addressing identified challenges and setting directions for future research. The framework leverages Neural Ordinary Differential Equations and Physics-Informed Neural Networks to enhance Physiologically Based Pharmacokinetic models while applying reinforcement learning algorithms to iteratively refine treatment policies based on patient-specific data. |
| 2025-05-14 | Radon Exposure Dataset | Dakotah Maguire, Jeremy Logan, Heechan Lee et.al. | 2505.09489 | | 7 pages, 2 tables | Abstract (click to expand)Exposure to elevated radon levels in the home is one of the leading causes of lung cancer in the world. The following study describes the creation of a comprehensive, state-level dataset designed to enable the modeling and prediction of household radon concentrations at Zip Code Tabulation Area (ZCTA) and sub-kilometer scales. Details include the data collection and processing involved in compiling physical and demographic factors for Pennsylvania and Utah. Attempting to mitigate this risk requires identifying the underlying geological causes and the populations that might be at risk. This work focuses on identifying at-risk populations throughout Pennsylvania and Utah, where radon levels are some of the highest in the country. The resulting dataset harmonizes geological and demographic factors from various sources and spatial resolutions, including temperature, geochemistry, and soil characteristics. Demographic variables such as the household heating fuel used, the age of building, and the housing type provide further insight into which populations could be most susceptible in areas with potentially high radon levels. This dataset also serves as a foundational resource for two other studies conducted by the authors. The resolution of the data provides a novel approach to predicting potential radon exposure, and the data processing conducted for these states can be scaled up to larger spatial resolutions (e.g., the Contiguous United States [CONUS]) and allow for a broad reclassification of radon exposure potential in the United States. |
| 2025-05-14 | Optimization of the initial post-buckling response of trusses and frames by an asymptotic approach | Federico Ferrari, Ole Sigmund et.al. | 2505.09373 | | | Abstract (click to expand)Asymptotic post-buckling theory is applied to sizing and topology optimization of trusses and frames, exploring its potential and current computational difficulties. We show that a designs' post-buckling response can be controlled by including the lowest two asymptotic coefficients, representing the initial post-buckling slope and curvature, in the optimization formulation. This also reduces the imperfection sensitivity of the optimized design. The asymptotic expansion can further be used to approximate the structural nonlinear response, and then to optimize for a given measure of the nonlinear mechanical performance such as, for example, end-compliance or complementary work. Examples of linear and nonlinear compliance minimization of trusses and frames show the effective use of the asymptotic method for including post-buckling constraints in structural optimization. |
| 2025-05-13 | Predictive Digital Twins with Quantified Uncertainty for Patient-Specific Decision Making in Oncology | Graham Pash, Umberto Villa, David A. Hormuth II et.al. | 2505.08927 | link | | Abstract (click to expand)Quantifying the uncertainty in predictive models is critical for establishing trust and enabling risk-informed decision making for personalized medicine. In contrast to one-size-fits-all approaches that seek to mitigate risk at the population level, digital twins enable personalized modeling thereby potentially improving individual patient outcomes. Realizing digital twins in biomedicine requires scalable and efficient methods to integrate patient data with mechanistic models of disease progression. This study develops an end-to-end data-to-decisions methodology that combines longitudinal non-invasive imaging data with mechanistic models to estimate and predict spatiotemporal tumor progression accounting for patient-specific anatomy. Through the solution of a statistical inverse problem, imaging data inform the spatially varying parameters of a reaction-diffusion model of tumor progression. An efficient parallel implementation of the forward model coupled with a scalable approximation of the Bayesian posterior distribution enables rigorous, but tractable, quantification of uncertainty due to the sparse, noisy measurements. The methodology is verified on a virtual patient with synthetic data to control for model inadequacy, noise level, and the frequency of data collection. The application to decision-making is illustrated by evaluating the importance of imaging frequency and formulating an optimal experimental design question. The clinical relevance is demonstrated through a model validation study on a cohort of patients with publicly available longitudinal imaging data. |
| 2025-05-13 | Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles | Junghoon Justin Park, Jiook Cha, Samuel Yen-Chi Chen et.al. | 2505.08782 | | | Abstract (click to expand)Quantum Machine Learning (QML) holds significant promise for solving computational challenges across diverse domains. However, its practical deployment is constrained by the limitations of noisy intermediate-scale quantum (NISQ) devices, including noise, limited scalability, and trainability issues in variational quantum circuits (VQCs). We introduce the multi-chip ensemble VQC framework, which partitions high-dimensional computations across smaller quantum chips to enhance scalability, trainability, and noise resilience. We show that this approach mitigates barren plateaus, reduces quantum error bias and variance, and maintains robust generalization through controlled entanglement. Designed to align with current and emerging quantum hardware, the framework demonstrates strong potential for enabling scalable QML on near-term devices, as validated by experiments on standard benchmark datasets (MNIST, FashionMNIST, CIFAR-10) and real world dataset (PhysioNet EEG). |
| 2025-05-14 | Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations | Abdolmehdi Behroozi, Chaopeng Shen and, Daniel Kifer et.al. | 2505.08740 | link | | Abstract (click to expand)Parametric differential equations of the form du/dt = f(u, x, t, p) are fundamental in science and engineering. While deep learning frameworks such as the Fourier Neural Operator (FNO) can efficiently approximate solutions, they struggle with inverse problems, sensitivity estimation (du/dp), and concept drift. We address these limitations by introducing a sensitivity-based regularization strategy, called Sensitivity-Constrained Fourier Neural Operators (SC-FNO). SC-FNO achieves high accuracy in predicting solution paths and consistently outperforms standard FNO and FNO with physics-informed regularization. It improves performance in parameter inversion tasks, scales to high-dimensional parameter spaces (tested with up to 82 parameters), and reduces both data and training requirements. These gains are achieved with a modest increase in training time (30% to 130% per epoch) and generalize across various types of differential equations and neural operators. Code and selected experiments are available at: https://github.com/AMBehroozi/SC_Neural_Operators |
| 2025-05-13 | Topology and geometry optimization of grid-shells under self-weight loading | Helen E. Fairclough, Karol Bolbotowski, Linwei He et.al. | 2505.08645 | | 25 pages, 20 figures | Abstract (click to expand)This manuscript presents an approach for simultaneously optimizing the connectivity and elevation of grid-shell structures acting in pure compression (or pure tension) under the combined effects of a prescribed external loading and the design-dependent self-weight of the structure itself. The method derived herein involves solving a second-order cone optimization problem, thereby ensuring convexity and obtaining globally optimal results for a given discretization of the design domain. Several numerical examples are presented, illustrating characteristics of this class of optimal structures. It is found that, as self-weight becomes more significant, both the optimal topology and the optimal elevation profile of the structure change, highlighting the importance of optimizing both topology and geometry simultaneously from the earliest stages of design. It is shown that this approach can obtain solutions with greater accuracy and several orders of magnitude more quickly than a standard 3D layout/truss topology optimization approach. |
| 2025-05-13 | Improving Unsupervised Task-driven Models of Ventral Visual Stream via Relative Position Predictivity | Dazhong Rong, Hao Dong, Xing Gao et.al. | 2505.08316 | link | This paper has been accepted for full publication at CogSci 2025 (https://cognitivesciencesociety.org/cogsci-2025/) | Abstract (click to expand)Based on the concept that ventral visual stream (VVS) mainly functions for object recognition, current unsupervised task-driven methods model VVS by contrastive learning, and have achieved good brain similarity. However, we believe functions of VVS extend beyond just object recognition. In this paper, we introduce an additional function involving VVS, named relative position (RP) prediction. We first theoretically explain contrastive learning may be unable to yield the model capability of RP prediction. Motivated by this, we subsequently integrate RP learning with contrastive learning, and propose a new unsupervised task-driven method to model VVS, which is more inline with biological reality. We conduct extensive experiments, demonstrating that: (i) our method significantly improves downstream performance of object recognition while enhancing RP predictivity; (ii) RP predictivity generally improves the model brain similarity. Our results provide strong evidence for the involvement of VVS in location perception (especially RP prediction) from a computational perspective. |
| 2025-05-12 | QUEST: QUantum-Enhanced Shared Transportation | Chinonso Onah, Neel Miscasci, Carsten Othmer et.al. | 2505.08074 | | 11 pages, 7 figures | Abstract (click to expand)We introduce Windbreaking-as-a-Service'' (WaaS) as an innovative approach to shared transportation in which largerwindbreaker'' vehicles provide aerodynamic shelter for ``windsurfer'' vehicles, thereby reducing drag and fuel consumption. As a computational framework to solve the large-scale matching and assignment problems that arise in WaaS, we present \textbf{QUEST} (Quantum-Enhanced Shared Transportation). Specifically, we formulate the pairing of windbreakers and windsurfers -- subject to timing, speed, and vehicle-class constraints -- as a mixed-integer quadratic problem (MIQP). Focusing on a single-segment prototype, we verify the solution classically via the Hungarian Algorithm, a Gurobi-based solver, and brute-force enumeration of binary vectors. We then encode the problem as a Quadratic Unconstrained Binary Optimization (QUBO) and map it to an Ising Hamiltonian, enabling the use of the Quantum Approximate Optimization Algorithm (QAOA) and other quantum and classical annealing technologies. Our quantum implementation successfully recovers the optimal assignment identified by the classical methods, confirming the soundness of the QUEST pipeline for a controlled prototype. While QAOA and other quantum heuristics do not guarantee a resolution of the fundamental complexity barriers, this study illustrates how the WaaS problem can be systematically translated into a quantum-ready model. It also lays the groundwork for addressing multi-segment scenarios and potentially leveraging quantum advantage for large-scale shared-transportation instances. |
| 2025-05-12 | A comparative study of Bitcoin and Ripple cryptocurrencies trading using Deep Reinforcement Learning algorithms | Dieu-Donne Fangnon, Armandine Sorel Kouyim Meli, Verlon Roel Mbingui et.al. | 2505.07660 | link | arXiv admin note: text overlap with arXiv:1911.10107 by other authors | Abstract (click to expand)Artificial intelligence (AI) has demonstrated remarkable success across various applications. In light of this trend, the field of automated trading has developed a keen interest in leveraging AI techniques to forecast the future prices of financial assets. This interest stems from the need to address trading challenges posed by the inherent volatility and dynamic nature of asset prices. However, crafting a flawless strategy becomes a formidable task when dealing with assets characterized by intricate and ever-changing price dynamics. To surmount these formidable challenges, this research employs an innovative rule-based strategy approach to train Deep Reinforcement Learning (DRL). This application is carried out specifically in the context of trading Bitcoin (BTC) and Ripple (XRP). Our proposed approach hinges on the integration of Deep Q-Network, Double Deep Q-Network, Dueling Deep Q-learning networks, alongside the Advantage Actor-Critic algorithms. Each of them aims to yield an optimal policy for our application. To evaluate the effectiveness of our Deep Reinforcement Learning (DRL) approach, we rely on portfolio wealth and the trade signal as performance metrics. The experimental outcomes highlight that Duelling and Double Deep Q-Network outperformed when using XRP with the increasing of the portfolio wealth. All codes are available in this \href{https://github.com/VerlonRoelMBINGUI/RL_Final_Projects_AMMI2023}{\color{blue}Github link}. |
| 2025-05-12 | A Value of Information-based assessment of strain-based thickness loss monitoring in ship hull structures | Nicholas E. Silionis, Konstantinos N. Anyfantis et.al. | 2505.07427 | | 32 pages, 16 figures, Preprint submitted to Elsevier journal | Abstract (click to expand)Recent advances in Structural Health Monitoring (SHM) have attracted industry interest, yet real-world applications, such as in ship structures remain scarce. Despite SHM's potential to optimise maintenance, its adoption in ships is limited due to the lack of clearly quantifiable benefits for hull maintenance. This study employs a Bayesian pre-posterior decision analysis to quantify the value of information (VoI) from SHM systems monitoring corrosion-induced thickness loss (CITL) in ship hulls, in a first-of-its-kind analysis for ship structures. We define decision-making consequence cost functions based on exceedance probabilities relative to a target CITL threshold, which can be set by the decision-maker. This introduces a practical aspect to our framework, that enables implicitly modelling the decision-maker's risk perception. We apply this framework to a large-scale, high-fidelity numerical model of a commercial vessel and examine the relative benefits of different CITL monitoring strategies, including strain-based SHM and traditional on-site inspections. |
| 2025-05-14 | Simulating many-engine spacecraft: Exceeding 100 trillion grid points via information geometric regularization and the MFC flow solver | Benjamin Wilfong, Anand Radhakrishnan, Henry Le Berre et.al. | 2505.07392 | link | 10 pages, 7 figures | Abstract (click to expand)This work proposes a method and optimized implementation for exascale simulations of high-speed compressible fluid flows, enabling the simulation of multi-engine rocket craft at an unprecedented scale. We significantly improve upon the state-of-the-art in terms of computational cost and memory footprint through a carefully crafted implementation of the recently proposed information geometric regularization, which eliminates the need for numerical shock capturing. Unified addressing on tightly coupled CPU--GPU platforms increases the total problem size with negligible performance hit. Despite linear stencil algorithms being memory-bound, we achieve wall clock times that are four times faster than optimized baseline numerics. This enables the execution of CFD simulations at more than 100 trillion grid points, surpassing the largest state-of-the-art publicly available simulations by an order of magnitude. Ideal weak scaling is demonstrated on OLCF Frontier and CSCS Alps using the full system, entailing 37.8K AMD MI250X GPUs (Frontier) or 9.2K NVIDIA GH200 superchips (Alps). |
| 2025-05-11 | Can LLM-based Financial Investing Strategies Outperform the Market in Long Run? | Weixian Waylon Li, Hyeonjun Kim, Mihai Cucuringu et.al. | 2505.07078 | | 14 pages | Abstract (click to expand)Large Language Models (LLMs) have recently been leveraged for asset pricing tasks and stock trading applications, enabling AI agents to generate investment decisions from unstructured financial data. However, most evaluations of LLM timing-based investing strategies are conducted on narrow timeframes and limited stock universes, overstating effectiveness due to survivorship and data-snooping biases. We critically assess their generalizability and robustness by proposing FINSABER, a backtesting framework evaluating timing-based strategies across longer periods and a larger universe of symbols. Systematic backtests over two decades and 100+ symbols reveal that previously reported LLM advantages deteriorate significantly under broader cross-section and over a longer-term evaluation. Our market regime analysis further demonstrates that LLM strategies are overly conservative in bull markets, underperforming passive benchmarks, and overly aggressive in bear markets, incurring heavy losses. These findings highlight the need to develop LLM strategies that are able to prioritise trend detection and regime-aware risk controls over mere scaling of framework complexity. |
| 2025-05-11 | Energy-Efficient Ternary Encoding for High-Speed Data Transmission in 3D-Integrated Circuits Using Inductive Coupling Links | Abdullah Saeed Alghotmi et.al. | 2505.06908 | | 6 pages | Abstract (click to expand)This paper proposes a ternary signalling scheme for inductive coupling links (ICLs) in 3D-integrated circuits (3D-ICs) to reduce crosstalk and electromagnetic interference in multi-stacked chip communications. By converting binary data into ternary sequences with three voltage levels (-V, 0V, +V), the approach enhances signal separation, reduces crosstalk, and improves signal integrity. Unlike traditional Non-Return to Zero (NRZ) systems, the ternary scheme increases bandwidth efficiency and reduces power consumption through fewer signal transitions. A modified H-Bridge transmitter generates ternary symbols by controlling current flow based on binary-to-ternary mapping. Preliminary simulations validate the efficiency of the scheme, showing reduced power consumption and higher data rates compared to NRZ. This approach shows promise for high-performance computing and IoT devices in 3D-IC environments, offering enhanced noise resilience, lower power usage, and improved communication efficiency. |
| 2025-05-10 | PC-SRGAN: Physically Consistent Super-Resolution Generative Adversarial Network for General Transient Simulations | Md Rakibul Hasan, Pouria Behnoudfar, Dan MacKinlay et.al. | 2505.06502 | | | Abstract (click to expand)Machine Learning, particularly Generative Adversarial Networks (GANs), has revolutionised Super Resolution (SR). However, generated images often lack physical meaningfulness, which is essential for scientific applications. Our approach, PC-SRGAN, enhances image resolution while ensuring physical consistency for interpretable simulations. PC-SRGAN significantly improves both the Peak Signal-to-Noise Ratio and the Structural Similarity Index Measure compared to conventional methods, even with limited training data (e.g., only 13% of training data required for SRGAN). Beyond SR, PC-SRGAN augments physically meaningful machine learning, incorporating numerically justified time integrators and advanced quality metrics. These advancements promise reliable and causal machine-learning models in scientific domains. A significant advantage of PC-SRGAN over conventional SR techniques is its physical consistency, which makes it a viable surrogate model for time-dependent problems. PC-SRGAN advances scientific machine learning, offering improved accuracy and efficiency for image processing, enhanced process understanding, and broader applications to scientific research. The source codes and data will be made publicly available at https://github.com/hasan-rakibul/PC-SRGAN upon acceptance of this paper. |
| 2025-05-09 | A New DAPO Algorithm for Stock Trading | Ruijian Zha, Bojun Liu et.al. | 2505.06408 | link | Accepted to IEEE IDS 2025 Special Track: Financial Reinforcement Learning and Foundation Models (FinRLFM). 3 pages, 2 figures, 3 tables | Abstract (click to expand)Recent advances in reinforcement learning, such as Dynamic Sampling Policy Optimization (DAPO), show strong performance when paired with large language models (LLMs). Motivated by this success, we ask whether similar gains can be realized in financial trading. We design a trading agent that combines an improved Group Relative Policy Optimization (GRPO) algorithm, augmented with ideas from DAPO, with LLM-based risk and sentiment signals extracted from financial news. On the NASDAQ-100 index (FNSPID dataset), our agent attains a cumulative return of 230.49 percent and an information ratio of 0.37, outperforming the CPPO-DeepSeek baseline. It also cuts training time from about 8 hours to 2.5 hours over 100 epochs while markedly reducing RAM usage. The proposed RL-LLM framework offers a scalable path toward data-efficient trading agents. Code: https://github.com/Ruijian-Zha/FinRL-DAPO-SR/ |
| 2025-05-07 | RAAC panels can suddenly collapse before any warning of corrosion-induced surface cracking | E. Korec, P. Grassl, M. Jirasek et.al. | 2505.06294 | | | Abstract (click to expand)The collapse of reinforced autoclaved aerated concrete (RAAC) panels has attracted considerable public and academic interest. As detailed experimental data are not yet available and replicating the natural corrosion process requires years or decades, computational modelling is essential to understand under which conditions corrosion remains concealed. The very high porosity of RAAC is widely suspected to be a major contributing factor. However, current corrosion-induced cracking models are known to struggle with capturing the role of concrete porosity. To remedy this critical deficiency, we propose to enrich corrosion-induced cracking modelling with the analytical solution of reactive transport equations governing the precipitation of rust and a porosity-dependent description of diffusivity. With this, the corrosion concealment in RAAC panels is studied computationally for the first time, revealing that RAAC panels can suddenly collapse before any warning of corrosion-induced surface cracking and allowing to map the conditions most likely to result in sudden collapse. |
| 2025-05-07 | ALFEE: Adaptive Large Foundation Model for EEG Representation | Wei Xiong, Junming Lin, Jiangtong Li et.al. | 2505.06291 | | 17pages, 17 figures | Abstract (click to expand)While foundation models excel in text, image, and video domains, the critical biological signals, particularly electroencephalography(EEG), remain underexplored. EEG benefits neurological research with its high temporal resolution, operational practicality, and safety profile. However, low signal-to-noise ratio, inter-subject variability, and cross-paradigm differences hinder the generalization of current models. Existing methods often employ simplified strategies, such as a single loss function or a channel-temporal joint representation module, and suffer from a domain gap between pretraining and evaluation tasks that compromises efficiency and adaptability. To address these limitations, we propose the Adaptive Large Foundation model for EEG signal representation(ALFEE) framework, a novel hybrid transformer architecture with two learning stages for robust EEG representation learning. ALFEE employs a hybrid attention that separates channel-wise feature aggregation from temporal dynamics modeling, enabling robust EEG representation with variable channel configurations. A channel encoder adaptively compresses variable channel information, a temporal encoder captures task-guided evolution, and a hybrid decoder reconstructs signals in both temporal and frequency domains. During pretraining, ALFEE optimizes task prediction, channel and temporal mask reconstruction, and temporal forecasting to enhance multi-scale and multi-channel representation. During fine-tuning, a full-model adaptation with a task-specific token dictionary and a cross-attention layer boosts performance across multiple tasks. After 25,000 hours of pretraining, extensive experimental results on six downstream EEG tasks demonstrate the superior performance of ALFEE over existing models. Our ALFEE framework establishes a scalable foundation for biological signal analysis with implementation at https://github.com/xw1216/ALFEE. |
| 2025-05-09 | FlowHFT: Flow Policy Induced Optimal High-Frequency Trading under Diverse Market Conditions | Yang Li, Zhi Chen, Steve Yang et.al. | 2505.05784 | | 14 pages, 1 figure, 6 tables, 2 algorithms | Abstract (click to expand)High-frequency trading (HFT) is an investing strategy that continuously monitors market states and places bid and ask orders at millisecond speeds. Traditional HFT approaches fit models with historical data and assume that future market states follow similar patterns. This limits the effectiveness of any single model to the specific conditions it was trained for. Additionally, these models achieve optimal solutions only under specific market conditions, such as assumptions about stock price's stochastic process, stable order flow, and the absence of sudden volatility. Real-world markets, however, are dynamic, diverse, and frequently volatile. To address these challenges, we propose the FlowHFT, a novel imitation learning framework based on flow matching policy. FlowHFT simultaneously learns strategies from numerous expert models, each proficient in particular market scenarios. As a result, our framework can adaptively adjust investment decisions according to the prevailing market state. Furthermore, FlowHFT incorporates a grid-search fine-tuning mechanism. This allows it to refine strategies and achieve superior performance even in complex or extreme market scenarios where expert strategies may be suboptimal. We test FlowHFT in multiple market environments. We first show that flow matching policy is applicable in stochastic market environments, thus enabling FlowHFT to learn trading strategies under different market conditions. Notably, our single framework consistently achieves performance superior to the best expert for each market condition. |
| 2025-05-09 | Unfitted finite element modelling of surface-bulk viscous flows in animal cells | Eric Neiva, Hervé Turlier et.al. | 2505.05723 | | 29 pages, 15 figures | Abstract (click to expand)This work presents a novel unfitted finite element framework to simulate coupled surface-bulk problems in time-dependent domains, focusing on fluid-fluid interactions in animal cells between the actomyosin cortex and the cytoplasm. The cortex, a thin layer beneath the plasma membrane, provides structural integrity and drives shape changes by generating surface contractile forces akin to tension. Cortical contractions generate Marangoni-like surface flows and induce intracellular cytoplasmic flows that are essential for processes such as cell division, migration, and polarization, particularly in large animal cells. Despite its importance, the spatiotemporal regulation of cortex-cytoplasm interactions remains poorly understood and computational modelling can be very challenging because surface-bulk dynamics often lead to large cell deformations. To address these challenges, we propose a sharp-interface framework that uniquely combines the trace finite element method for surface flows with the aggregated finite element method for bulk flows. This approach enables accurate and stable simulations on fixed Cartesian grids without remeshing. The model also incorporates mechanochemical feedback through the surface transport of a molecular regulator of active tension. We solve the resulting mixed-dimensional system on a fixed Cartesian grid using a level-set-based method to track the evolving surface. Numerical experiments validate the accuracy and stability of the method, capturing phenomena such as self-organised pattern formation, curvature-driven relaxation, and cell cleavage. This novel framework offers a powerful and extendable tool for investigating increasingly complex morphogenetic processes in animal cells. |
| 2025-05-08 | Characterizing GPU Energy Usage in Exascale-Ready Portable Science Applications | William F. Godoy, Oscar Hernandez, Paul R. C. Kent et.al. | 2505.05623 | | 14 pages, 8 figures, 3 tables. Accepted at the Energy Efficiency with Sustainable Performance: Techniques, Tools, and Best Practices, EESP Workshop, in conjunction with ISC High Performance 2025 | Abstract (click to expand)We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QMCPACK, a quantum Monte Carlo package, and (ii) AMReX-Castro, an adaptive mesh astrophysical code. We analyze power, temperature, utilization, and energy traces from double-/single (mixed)-precision benchmarks on NVIDIA's A100 and H100 and AMD's MI250X GPUs using queries in NVML and rocm smi lib, respectively. We explore application-specific metrics to provide insights on energy vs. performance trade-offs. Our results suggest that mixed-precision energy savings range between 6-25% on QMCPACK and 45% on AMReX-Castro. Also there are still gaps in the AMD tooling on Frontier GPUs that need to be understood, while query resolutions on NVML have little variability between 1 ms and 1 s. Overall, application level knowledge is crucial to define energy-cost/science-benefit opportunities for the codesign of future supercomputer architectures in the post-Moore era. |
| 2025-05-08 | The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion | Andrew Qiu, Shubham Barhate, Hin Wai Lui et.al. | 2505.05605 | | | Abstract (click to expand)Deep learning for conversion prediction has found widespread applications in online advertising. These models have become more complex as they are trained to jointly predict multiple objectives such as click, add-to-cart, checkout and other conversion types. Additionally, the capacity and performance of these models can often be increased with the use of embedding tables that encode high cardinality categorical features such as advertiser, user, campaign, and product identifiers (IDs). These embedding tables can be pre-trained, but also learned end-to-end jointly with the model to directly optimize the model objectives. Training these large tables is challenging due to: gradient sparsity, the high cardinality of the categorical features, the non-uniform distribution of IDs and the very high label sparsity. These issues make training prone to both slow convergence and overfitting after the first epoch. Previous works addressed the multi-epoch overfitting issue by using: stronger feature hashing to reduce cardinality, filtering of low frequency IDs, regularization of the embedding tables, re-initialization of the embedding tables after each epoch, etc. Some of these techniques reduce overfitting at the expense of reduced model performance if used too aggressively. In this paper, we share key learnings from the development of embedding table optimization and multi-epoch training in Pinterest Ads Conversion models. We showcase how our Sparse Optimizer speeds up convergence, and how multi-epoch overfitting varies in severity between different objectives in a multi-task model depending on label sparsity. We propose a new approach to deal with multi-epoch overfitting: the use of a frequency-adaptive learning rate on the embedding tables and compare it to embedding re-initialization. We evaluate both methods offline using an industrial large-scale production dataset. |
| 2025-05-08 | Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer | Wenhao Guo, Yuda Wang, Zeqiao Huang et.al. | 2505.05595 | | 16 pages, 12 figures | Abstract (click to expand)In the complex landscape of traditional futures trading, where vast data and variables like real-time Limit Order Books (LOB) complicate price predictions, we introduce the FutureQuant Transformer model, leveraging attention mechanisms to navigate these challenges. Unlike conventional models focused on point predictions, the FutureQuant model excels in forecasting the range and volatility of future prices, thus offering richer insights for trading strategies. Its ability to parse and learn from intricate market patterns allows for enhanced decision-making, significantly improving risk management and achieving a notable average gain of 0.1193% per 30-minute trade over state-of-the-art models with a simple algorithm using factors such as RSI, ATR, and Bollinger Bands. This innovation marks a substantial leap forward in predictive analytics within the volatile domain of futures trading. |
| 2025-05-08 | Advanced Stock Market Prediction Using Long Short-Term Memory Networks: A Comprehensive Deep Learning Framework | Rajneesh Chaudhary et.al. | 2505.05325 | | 11 pages, 17 figures, submitted as a pre-final year undergraduate project at Indian Institute of Information Technology, Gwalior. The paper integrates LSTM-based time series forecasting with sentiment analysis using VADER and includes a working web interface for real-time prediction | Abstract (click to expand)Predicting stock market movements remains a persistent challenge due to the inherently volatile, non-linear, and stochastic nature of financial time series data. This paper introduces a deep learning-based framework employing Long Short-Term Memory (LSTM) networks to forecast the closing stock prices of major technology firms: Apple, Google, Microsoft, and Amazon, listed on NASDAQ. Historical data was sourced from Yahoo Finance and processed using normalization and feature engineering techniques. The proposed model achieves a Mean Absolute Percentage Error (MAPE) of 2.72 on unseen test data, significantly outperforming traditional models like ARIMA. To further enhance predictive accuracy, sentiment scores were integrated using real-time news articles and social media data, analyzed through the VADER sentiment analysis tool. A web application was also developed to provide real-time visualizations of stock price forecasts, offering practical utility for both individual and institutional investors. This research demonstrates the strength of LSTM networks in modeling complex financial sequences and presents a novel hybrid approach combining time series modeling with sentiment analysis. |
| 2025-05-13 | A thermoelastic plate model for shot peen forming metal panels based on effective torque | Conor Rowan et.al. | 2505.05236 | | | Abstract (click to expand)A common technique used in factories to shape metal panels is shot peen forming. The impacts between the hard steel shot and the softer metal of the panel cause localized plastic deformation used to improve the fatigue properties of the material's surface. The residual stress distribution imparted by impacts also results in bending, which suggests that a torque is associated with it. In this paper, we model shot peen forming as the application of spatially varying torques to a Kirchhoff plate, opting to use the language of thermoelasticity in order to introduce these torque distributions. First, we derive the governing equations for the thermoelastic thin plate model and show that only a torque-type resultant of the temperature distribution shows up in the bending equation. Next, to calibrate from the shot peen operation an empirical effective torque parameter used in the thermoelastic model, a simple and non-invasive test is devised. This test relies only on measuring the maximum displacement of a uniformly shot peened plate as opposed to characterizing the residual stress distribution. After discussing how to handle the unconventional fully-free boundary conditions germane for peened plates, we introduce an approach to solving the inverse problem whereby the peening distribution required to obtain a specified plate contour can be obtained. Given the non-unique relationship between peening distributions and the displacement at discrete points, we explore a regularization of the inverse problem which gives rise to shot peen distributions that match the capabilities of equipment in the factory. In order to validate our proposed model, an experiment with quantified uncertainty is designed and carried out which investigates the agreement between the predictions of the calibrated model and real shot peen forming operations. |
| 2025-05-08 | Physics-informed solution reconstruction in elasticity and heat transfer using the explicit constraint force method | Conor Rowan, Kurt Maute, Alireza Doostan et.al. | 2505.04875 | | | Abstract (click to expand)One use case of physics-informed neural networks'' (PINNs) is solution reconstruction, which aims to estimate the full-field state of a physical system from sparse measurements. Parameterized governing equations of the system are used in tandem with the measurements to regularize the regression problem. However, in real-world solution reconstruction problems, the parameterized governing equation may be inconsistent with the physical phenomena that give rise to the measurement data. We show that due to assuming consistency between the true and parameterized physics, PINNs-based approaches may fail to satisfy three basic criteria of interpretability, robustness, and data consistency. As we argue, these criteria ensure that (i) the quality of the reconstruction can be assessed, (ii) the reconstruction does not depend strongly on the choice of physics loss, and (iii) that in certain situations, the physics parameters can be uniquely recovered. In the context of elasticity and heat transfer, we demonstrate how standard formulations of the physics loss and techniques for constraining the solution to respect the measurement data lead to differentconstraint forces" -- which we define as additional source terms arising from the constraints -- and that these constraint forces can significantly influence the reconstructed solution. To avoid the potentially substantial influence of the choice of physics loss and method of constraint enforcement on the reconstructed solution, we propose the ``explicit constraint force method'' (ECFM) to gain control of the source term introduced by the constraint. We then show that by satisfying the criteria of interpretability, robustness, and data consistency, this approach leads to more predictable and customizable reconstructions from noisy measurement data, even when the parameterization of the missing physics is inconsistent with the measured system. |
| 2025-05-07 | HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights | Ozan Gokdemir, Carlo Siebenschuh, Alexander Brace et.al. | 2505.04846 | | This paper has been accepted at the Platform for Advanced Scientific Computing Conference (PASC 25), June 16-18, 2025, Brugg-Windisch, Switzerland | Abstract (click to expand)The volume of scientific literature is growing exponentially, leading to underutilized discoveries, duplicated efforts, and limited cross-disciplinary collaboration. Retrieval Augmented Generation (RAG) offers a way to assist scientists by improving the factuality of Large Language Models (LLMs) in processing this influx of information. However, scaling RAG to handle millions of articles introduces significant challenges, including the high computational costs associated with parsing documents and embedding scientific knowledge, as well as the algorithmic complexity of aligning these representations with the nuanced semantics of scientific content. To address these issues, we introduce HiPerRAG, a RAG workflow powered by high performance computing (HPC) to index and retrieve knowledge from more than 3.6 million scientific articles. At its core are Oreo, a high-throughput model for multimodal document parsing, and ColTrast, a query-aware encoder fine-tuning algorithm that enhances retrieval accuracy by using contrastive learning and late-interaction techniques. HiPerRAG delivers robust performance on existing scientific question answering benchmarks and two new benchmarks introduced in this work, achieving 90% accuracy on SciQ and 76% on PubMedQA-outperforming both domain-specific models like PubMedGPT and commercial LLMs such as GPT-4. Scaling to thousands of GPUs on the Polaris, Sunspot, and Frontier supercomputers, HiPerRAG delivers million document-scale RAG workflows for unifying scientific knowledge and fostering interdisciplinary innovation. |
| 2025-05-07 | RDPP-TD: Reputation and Data Privacy-Preserving based Truth Discovery Scheme in Mobile Crowdsensing | Lijian Wu, Weikun Xie, Wei Tan et.al. | 2505.04361 | | | Abstract (click to expand)Truth discovery (TD) plays an important role in Mobile Crowdsensing (MCS). However, existing TD methods, including privacy-preserving TD approaches, estimate the truth by weighting only the data submitted in the current round, which often results in low data quality. Moreover, there is a lack of effective TD methods that preserve both reputation and data privacy. To address these issues, a Reputation and Data Privacy-Preserving based Truth Discovery (RDPP-TD) scheme is proposed to obtain high-quality data for MCS. The RDPP-TD scheme consists of two key approaches: a Reputation-based Truth Discovery (RTD) approach, which integrates the weight of current-round data with workers' reputation values to estimate the truth, thereby achieving more accurate results, and a Reputation and Data Privacy-Preserving (RDPP) approach, which ensures privacy preservation for sensing data and reputation values. First, the RDPP approach, when seamlessly integrated with RTD, can effectively evaluate the reliability of workers and their sensing data in a privacy-preserving manner. Second, the RDPP scheme supports reputation-based worker recruitment and rewards, ensuring high-quality data collection while incentivizing workers to provide accurate information. Comprehensive theoretical analysis and extensive experiments based on real-world datasets demonstrate that the proposed RDPP-TD scheme provides strong privacy protection and improves data quality by up to 33.3%. |
| 2025-05-07 | Yield and Buckling Stress Limits in Topology Optimization of Multiscale Structures | Christoffer Fyllgraf Christensen, Fengwen Wang, Ole Sigmund et.al. | 2505.04353 | | 24 pages, 20 figures, 9 tables | Abstract (click to expand)This study presents an extension of multiscale topology optimization by integrating both yield stress and local/global buckling considerations into the design process. Building upon established multiscale methodologies, we develop a new framework incorporating yield stress limits either as constraints or objectives alongside previously established local and global buckling constraints. This approach significantly refines the optimization process, ensuring that the resulting designs meet mechanical performance criteria and adhere to critical material yield constraints. First, we establish local density-dependent von Mises yield surfaces based on local yield estimates from homogenization-based analysis to predict the local yield limits of the homogenized materials. Then, these local Yield-based Load Factors (YLFs) are combined with local and global buckling criteria to obtain topology optimized designs that consider yield and buckling failure on all levels. This integration is crucial for the practical application of optimized structures in real-world scenarios, where material yield and stability behavior critically influence structural integrity and durability. Numerical examples demonstrate how optimized designs depend on the stiffness to yield ratio of the considered building material. Despite the foundational assumption of separation of scales, the de-homogenized structures, even at relatively coarse length scales, exhibit a high degree of agreement with the corresponding homogenized predictions. |
| 2025-05-06 | Modal Decomposition and Identification for a Population of Structures Using Physics-Informed Graph Neural Networks and Transformers | Xudong Jian, Kiran Bacsa, Gregory Duthé et.al. | 2505.04018 | | | Abstract (click to expand)Modal identification is crucial for structural health monitoring and structural control, providing critical insights into structural dynamics and performance. This study presents a novel deep learning framework that integrates graph neural networks (GNNs), transformers, and a physics-informed loss function to achieve modal decomposition and identification across a population of structures. The transformer module decomposes multi-degrees-of-freedom (MDOF) structural dynamic measurements into single-degree-of-freedom (SDOF) modal responses, facilitating the identification of natural frequencies and damping ratios. Concurrently, the GNN captures the structural configurations and identifies mode shapes corresponding to the decomposed SDOF modal responses. The proposed model is trained in a purely physics-informed and unsupervised manner, leveraging modal decomposition theory and the independence of structural modes to guide learning without the need for labeled data. Validation through numerical simulations and laboratory experiments demonstrates its effectiveness in accurately decomposing dynamic responses and identifying modal properties from sparse structural dynamic measurements, regardless of variations in external loads or structural configurations. Comparative analyses against established modal identification techniques and model variations further underscore its superior performance, positioning it as a favorable approach for population-based structural health monitoring. |
| 2025-05-06 | Algorithm Selection in Short-Range Molecular Dynamics Simulations | Samuel James Newcome, Fabio Alexander Gratl, Manuel Lerchner et.al. | 2505.03438 | | 16 pages, 1 figure. Submitted to the 25th International Conference on Computational Science. This version includes two minor corrections to the submitted manuscript, which do not result from the conference's peer review, and no changes resulting from the peer review process | Abstract (click to expand)Numerous algorithms and parallelisations have been developed for short-range particle simulations; however, none are optimally performant for all scenarios. Such a concept led to the prior development of the particle simulation library AutoPas, which implemented many of these algorithms and parallelisations and could select and tune these over the course of the simulation as the scenario changed. Prior works have, however, used only naive approaches to the algorithm selection problem, which can lead to significant overhead from trialling poorly performing algorithmic configurations. In this work, we investigate this problem in the case of Molecular Dynamics simulations. We present three algorithm selection strategies: an approach which makes performance predictions from past data, an expert-knowledge fuzzy logic-based approach, and a data-driven random forest-based approach. We demonstrate that these approaches can achieve speedups of up to 4.05 compared to prior approaches and 1.25 compared to a perfect configuration selection without dynamic algorithm selection. In addition, we discuss the practicality of the strategies in comparison to their performance, to highlight the tractability of such solutions. |
| 2025-05-09 | Data-efficient inverse design of spinodoid metamaterials | Max Rosenkranz, Markus Kästner, Ivo F. Sbalzarini et.al. | 2505.03415 | | 17 pages, 11 figures (edited acknowledgements) | Abstract (click to expand)We create an data-efficient and accurate surrogate model for structure-property linkages of spinodoid metamaterials with only 75 data points -- far fewer than the several thousands used in prior works -- and demonstrate its use in multi-objective inverse design. The inverse problem of finding a material microstructure that leads to given bulk properties is of great interest in mechanics and materials science. These inverse design tasks often require a large dataset, which can become unaffordable when considering material behavior that requires more expensive simulations or experiments. We generate a data-efficient surrogate for the mapping between the characteristics of the local material structure and the effective elasticity tensor and use it to inversely design structures with multiple objectives simultaneously. The presented neural network-based surrogate model achieves its data efficiency by inherently satisfying certain requirements, such as equivariance with respect to permutations of structure parameters, which avoids having to learn them from data. The resulting surrogate of the forward model is differentiable, allowing its direct use in gradient-based optimization for the inverse design problem. We demonstrate in three inverse design tasks of varying complexity that this approach yields reliable results while requiring significantly less training data than previous approaches based on neural-network surrogates. This paves the way for inverse design involving nonlinear mechanical behavior, where data efficiency is currently the limiting factor. |
| 2025-05-06 | Transformers Applied to Short-term Solar PV Power Output Forecasting | Andea Scott, Sindhu Sreedhara, Folasade Ayoola et.al. | 2505.03188 | | | Abstract (click to expand)Reliable forecasts of the power output from variable renewable energy generators like solar photovoltaic systems are important to balancing load on real-time electricity markets and ensuring electricity supply reliability. However, solar PV power output is highly uncertain, with significant variations occurring over both longer (daily or seasonally) and shorter (within minutes) timescales due to weather conditions, especially cloud cover. This paper builds on existing work that uses convolutional neural networks in the computer vision task of predicting (in a Nowcast model) and forecasting (in a Forecast model) solar PV power output (Stanford EAO SUNSET Model). A pure transformer architecture followed by a fully-connected layer is applied to one year of image data with experiments run on various combinations of learning rate and batch size. We find that the transformer architecture performs almost as well as the baseline model in the PV output prediction task. However, it performs worse on sunny days. |
| 2025-05-05 | Multiscale Parallel Simulation of Malignant Pleural Mesothelioma via Adaptive Domain Partitioning -- an Efficiency Analysis Study | Anton Dolganov, Valeria Krzhizhanovskaya, Stefano Trebeschi et.al. | 2505.03067 | | | Abstract (click to expand)A novel parallel efficiency analysis on a framework for simulating the growth of Malignant Pleural Mesothelioma (MPM) tumours is presented. Proliferation of MPM tumours in the pleural space is simulated using a Cellular Potts Model (CPM) coupled with partial differential equations (PDEs). Using segmented lung data from CT scans, an environment is set up with artificial tumour data in the pleural space, representing the simulation domain, onto which a dynamic bounding box is applied to restrict computations to the region of interest, dramatically reducing memory and CPU overhead. This adaptive partitioning of the domain enables efficient use of computational resources by reducing the three-dimensional (3D) domain over which the PDEs are to be solved. The PDEs, representing oxygen, nutrients, and cytokines, are solved using the finite-volume method with a first-order implicit Euler scheme. Parallelization is realized using the public Python library mpi4py in combination with LinearGMRESSolver and PETSc for efficient convergence. Performance analyses have shown that parallelization achieves a reduced solving time compared to serial computation. Also, optimizations enable efficient use of available memory and improved load balancing amongst the cores. |
| 2025-05-05 | Data Compression for Time Series Modelling: A Case Study of Smart Grid Demand Forecasting | Mikkel Bue Lykkegaard, Svend Vendelbo Nielsen, Akanksha Upadhyay et.al. | 2505.02606 | | | Abstract (click to expand)Efficient time series forecasting is essential for smart energy systems, enabling accurate predictions of energy demand, renewable resource availability, and grid stability. However, the growing volume of high-frequency data from sensors and IoT devices poses challenges for storage and transmission. This study explores Discrete Wavelet Transform (DWT)-based data compression as a solution to these challenges while ensuring forecasting accuracy. A case study of a seawater supply system in Hirtshals, Denmark, operating under dynamic weather, operational schedules, and seasonal trends, is used for evaluation. Biorthogonal wavelets of varying orders were applied to compress data at different rates. Three forecasting models - Ordinary Least Squares (OLS), XGBoost, and the Time Series Dense Encoder (TiDE) - were tested to assess the impact of compression on forecasting performance. Lossy compression rates up to \(r_{\mathrm{lossy}} = 0.999\) were analyzed, with the Normalized Mutual Information (NMI) metric quantifying the relationship between compression and information retention. Results indicate that wavelet-based compression can retain essential features for accurate forecasting when applied carefully. XGBoost proved highly robust to compression artifacts, maintaining stable performance across diverse compression rates. In contrast, OLS demonstrated sensitivity to smooth wavelets and high compression rates, while TiDE showed some variability but remained competitive. This study highlights the potential of wavelet-based compression for scalable, efficient data management in smart energy systems without sacrificing forecasting accuracy. The findings are relevant to other fields requiring high-frequency time series forecasting, including climate modeling, water supply systems, and industrial operations. |
| 2025-05-05 | Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder | Ruikun Li, Jingwen Cheng, Huandong Wang et.al. | 2505.02450 | | | Abstract (click to expand)Predicting the dynamics of complex systems is crucial for various scientific and engineering applications. The accuracy of predictions depends on the model's ability to capture the intrinsic dynamics. While existing methods capture key dynamics by encoding a low-dimensional latent space, they overlook the inherent multiscale structure of complex systems, making it difficult to accurately predict complex spatiotemporal evolution. Therefore, we propose a Multiscale Diffusion Prediction Network (MDPNet) that leverages the multiscale structure of complex systems to discover the latent space of intrinsic dynamics. First, we encode multiscale features through a multiscale diffusion autoencoder to guide the diffusion model for reliable reconstruction. Then, we introduce an attention-based graph neural ordinary differential equation to model the co-evolution across different scales. Extensive evaluations on representative systems demonstrate that the proposed method achieves an average prediction error reduction of 53.23% compared to baselines, while also exhibiting superior robustness and generalization. |
| 2025-05-04 | Probabilistic Method for Optimizing Submarine Search and Rescue Strategy Under Environmental Uncertainty | Runhao Liu, Ziming Chen, Peng Zhang et.al. | 2505.02186 | | | Abstract (click to expand)When coping with the urgent challenge of locating and rescuing a deep-sea submersible in the event of communication or power failure, environmental uncertainty in the ocean can not be ignored. However, classic physical models are limited to deterministic scenarios. Therefore, we present a hybrid algorithm framework combined with dynamic analysis for target submarine, Monte Carlo and Bayesian method for conducting a probabilistic prediction to improve the search efficiency. Herein, the Monte Carlo is performed to overcome the environmental variability to improve the accuracy in location prediction. According to the trajectory prediction, we integrated the Bayesian based grid research and probabilistic updating. For more complex situations, we introduced the Bayesian filtering. Aiming to maximize the rate of successful rescue and costs, the economic optimization is performed utilizing the cost-benefit analysis based on entropy weight method and the CER is applied for evaluation. |
| 2025-05-04 | Data-Driven Team Selection in Fantasy Premier League Using Integer Programming and Predictive Modeling Approach | Danial Ramezani et.al. | 2505.02170 | | | Abstract (click to expand)Fantasy football is a billion-dollar industry with millions of participants. Constrained by a fixed budget, decision-makers draft a squad whose players are expected to perform well in the upcoming weeks to maximize total points. This paper proposes novel deterministic and robust integer programming models that select the optimal starting eleven and the captain. A new hybrid scoring metric is constructed using an interpretable artificial intelligence framework and underlying match performance data. Several objective functions and estimation techniques are introduced for the programming model. To the best of my knowledge, this is the first study to approach fantasy football through this lens. The models' performance is evaluated using data from the 2023/24 Premier League season. Results indicate that the proposed hybrid method achieved the highest score while maintaining consistent performance. Utilizing the Monte Carlo simulation, the strategic choice of averaging techniques for estimating cost vectors, and the proposed hybrid approach are shown to be effective during the out-of-sample period. This paper also provides a thorough analysis of the optimal formations and players selected by the models, offering valuable insights into effective fantasy football strategies. |
| 2025-05-04 | Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarking | Muyao Zhong, Yushi Lin, Peng Yang et.al. | 2505.02139 | | | Abstract (click to expand)The Limit Order Book (LOB), the mostly fundamental data of the financial market, provides a fine-grained view of market dynamics while poses significant challenges in dealing with the esteemed deep models due to its strong autocorrelation, cross-feature constrains, and feature scale disparity. Existing approaches often tightly couple representation learning with specific downstream tasks in an end-to-end manner, failed to analyze the learned representations individually and explicitly, limiting their reusability and generalization. This paper conducts the first systematic comparative study of LOB representation learning, aiming to identify the effective way of extracting transferable, compact features that capture essential LOB properties. We introduce LOBench, a standardized benchmark with real China A-share market data, offering curated datasets, unified preprocessing, consistent evaluation metrics, and strong baselines. Extensive experiments validate the sufficiency and necessity of LOB representations for various downstream tasks and highlight their advantages over both the traditional task-specific end-to-end models and the advanced representation learning models for general time series. Our work establishes a reproducible framework and provides clear guidelines for future research. Datasets and code will be publicly available at https://github.com/financial-simulation-lab/LOBench. |
| 2025-05-04 | A Deep Learning-Aided Approach for Estimating Field Permeability Map by Fusing Well Logs, Well Tests, and Seismic Data | Grigoriy Shutov, Viktor Duplyakov, Shadfar Davoodi et.al. | 2505.02093 | | | Abstract (click to expand)Obtaining reliable permeability maps of oil reservoirs is crucial for building a robust and accurate reservoir simulation model and, therefore, designing effective recovery strategies. This problem, however, remains challenging, as it requires the integration of various data sources by experts from different disciplines. Moreover, there are no sources to provide direct information about the inter-well space. In this work, a new method based on the data-fusion approach is proposed for predicting two-dimensional permeability maps on the whole reservoir area. This method utilizes non-parametric regression with a custom kernel shape accounting for different data sources: well logs, well tests, and seismics. A convolutional neural network is developed to process seismic data and then incorporate it with other sources. A multi-stage data fusion procedure helps to artificially increase the training dataset for the seismic interpretation model and finally to construct the adequate permeability map. The proposed methodology of permeability map construction from different sources was tested on a real oil reservoir located in Western Siberia. The results demonstrate that the developed map perfectly corresponds to the permeability estimations in the wells, and the inter-well space permeability predictions are considerably improved through the incorporation of the seismic data. |
| 2025-05-04 | A Deep Learning Scheme of Electromagnetic Scattering From Scatterers With Incomplete Profiles | Ji-Yuan Wang, Xin-Yue Lou, Liang Zhang et.al. | 2505.02086 | | | Abstract (click to expand)A deep learning scheme is proposed to solve the electromagnetic (EM) scattering problems where the profile of the dielectric scatterer of interest is incomplete. As a compensation, a limited amount of scattering data is provided, which is in principle containing sufficient information associated with the missing part of the profile. The existing solvers can hardly realize the compensation if the known part of the profile and the scattering data are combined straightforwardly. On one hand, the well-developed forward solvers have no mechanism to accept the scattering data, which can recover the unknown part of the profile if properly used. On the other hand, the existing solvers for inverse problems cannot retrieve the complete profile with an acceptable accuracy from the limited amount of scattering data, even when the available part of the profile can be fed into the solvers. This work aims to handle the difficulty. To this end, the EM forward scattering from an incompletely known dielectric scatterer is derived. A scheme based on DL is then proposed where the forward and inverse scattering problems are solved simultaneously. Numerical experiments are conducted to demonstrate the performance of the proposed DL-based scheme for both two-dimensional (2-D) and three-dimensional (3-D) EM scattering problems. |
| 2025-05-03 | A computational framework for predicting the effect of surface roughness in fatigue | S. Jiménez-Alfaro, E. Martínez-Pañeda et.al. | 2505.01871 | | | Abstract (click to expand)Surface roughness is a critical factor influencing the fatigue life of structural components. Its effect is commonly quantified using a correction coefficient known as the surface factor. In this paper, a phase field based numerical framework is proposed to estimate the surface factor while accounting for the stochastic nature of surface roughness. The model is validated against existing experimental data. Furthermore, we investigate the influence of key parameters on the fatigue life of rough surfaces, such as surface topology and failure strength. An important effect of surface roughness is observed when the average surface roughness increases and the correlation length of the surface profile decreases. This effect becomes more pronounced with higher failure strengths. |
| 2025-05-03 | Enhancing Black-Litterman Portfolio via Hybrid Forecasting Model Combining Multivariate Decomposition and Noise Reduction | Ziye Yang, Ke Lu et.al. | 2505.01781 | | | Abstract (click to expand)The sensitivity to input parameters and lack of flexibility limits the traditional Mean-Variance model. In contrast, the Black-Litterman model has attracted widespread attention by integrating market equilibrium returns with investors' subjective views. This paper proposes a novel hybrid deep learning model combining Singular Spectrum analysis (SSA), Multivariate Aligned Empirical Mode Decomposition (MA-EMD), and Temporal Convolutional Networks (TCNs), aiming to improve the prediction accuracy of asset prices and thus enhance the ability of the Black-Litterman model to generate subjective views. Experimental results show that noise reduction pre-processing can improve the model's accuracy, and the prediction performance of the proposed model is significantly better than that of three multivariate decomposition benchmark models. We construct an investment portfolio by using 20 representative stocks from the NASDAQ 100 index. By combining the hybrid forecasting model with the Black-Litterman model, the generated investment portfolio exhibits better returns and risk control capabilities than the Mean-Variance, Equal-Weighted, and Market-Weighted models in the short holding period. |
| 2025-05-02 | A Domain Adaptation of Large Language Models for Classifying Mechanical Assembly Components | Fatemeh Elhambakhsh, Daniele Grandi, Hyunwoong Ko et.al. | 2505.01627 | | | Abstract (click to expand)The conceptual design phase represents a critical early stage in the product development process, where designers generate potential solutions that meet predefined design specifications based on functional requirements. Functional modeling, a foundational aspect of this phase, enables designers to reason about product functions before specific structural details are determined. A widely adopted approach to functional modeling is the Function-Behavior-Structure (FBS) framework, which supports the transformation of functional intent into behavioral and structural descriptions. However, the effectiveness of function-based design is often hindered by the lack of well-structured and comprehensive functional data. This scarcity can negatively impact early design decision-making and hinder the development of accurate behavioral models. Recent advances in Large Language Models (LLMs), such as those based on GPT architectures, offer a promising avenue to address this gap. LLMs have demonstrated significant capabilities in language understanding and natural language processing (NLP), making them suitable for automated classification tasks. This study proposes a novel LLM-based domain adaptation (DA) framework using fine-tuning for the automated classification of mechanical assembly parts' functions. By fine-tuning LLMs on domain-specific datasets, the traditionally manual and subjective process of function annotation can be improved in both accuracy and consistency. A case study demonstrates fine-tuning GPT-3.5 Turbo on data from the Oregon State Design Repository (OSDR), and evaluation on the A Big CAD (ABC) dataset shows that the domain-adapted LLM can generate high-quality functional data, enhancing the semantic representation of mechanical parts and supporting more effective design exploration in early-phase engineering. |
| 2025-05-02 | Dynamical Update Maps for Particle Flow with Differential Algebra | Simone Servadio et.al. | 2505.01598 | | 8 pages, 9 figures, FUSION 2025 | Abstract (click to expand)Particle Flow Filters estimate the ``a posteriori" probability density function (PDF) by moving an ensemble of particles according to the likelihood. Particles are propagated under the system dynamics until a measurement becomes available when each particle undergoes an additional stochastic differential equation in a pseudo-time that updates the distribution following a homotopy transformation. This flow of particles can be represented as a recursive update step of the filter. In this work, we leverage the Differential Algebra (DA) representation of the solution flow of dynamics to improve the computational burden of particle flow filters. Thanks to this approximation, both the prediction and the update differential equations are solved in the DA framework, creating two sets of polynomial maps: the first propagates particles forward in time while the second updates particles, achieving the flow. The final result is a new particle flow filter that rapidly propagates and updates PDFs using mathematics based on deviation vectors. Numerical applications show the benefits of the proposed technique, especially in reducing computational time, so that small systems such as CubeSats can run the filter for attitude determination. |
| 2025-05-02 | Advances in Particle Flow Filters with Taylor Expansion Series | Simone Servadio et.al. | 2505.01597 | | 8 pages, 6 figures, FUSION 2025 | Abstract (click to expand)Particle Flow Filters perform the measurement update by moving particles to a different location rather than modifying the particles' weight based on the likelihood. Their movement (flow) is dictated by a drift term, which continuously pushes the particle toward the posterior distribution, and a diffusion term, which guarantees the spread of particles. This work presents a novel derivation of these terms based on high-order polynomial expansions, where the common techniques based on linearization reduce to a simpler version of the new methodology. Thanks to differential algebra, the high-order particle flow is derived directly onto the polynomials representation of the distribution, embedded with differentiation and evaluation. The resulting technique proposes two new particle flow filters, whose difference relies on the selection of the expansion center for the Taylor polynomial evaluation. Numerical applications show the improvement gained by the inclusion of high-order terms, especially when comparing performance with the Gromov flow and the "exact" flow. |
| 2025-05-02 | Optimising Kernel-based Multivariate Statistical Process Control | Zina-Sabrina Duma, Victoria Jorry, Tuomas Sihvonen et.al. | 2505.01556 | link | | Abstract (click to expand)Multivariate Statistical Process Control (MSPC) is a framework for monitoring and diagnosing complex processes by analysing the relationships between multiple process variables simultaneously. Kernel MSPC extends the methodology by leveraging kernel functions to capture non-linear relationships between the data, enhancing the process monitoring capabilities. However, optimising the kernel MSPC parameters, such as the kernel type and kernel parameters, is often done in literature in time-consuming and non-procedural manners such as cross-validation or grid search. In the present paper, we propose optimising the kernel MSPC parameters with Kernel Flows (KF), a recent kernel learning methodology introduced for Gaussian Process Regression (GPR). Apart from the optimisation technique, the novelty of the study resides also in the utilisation of kernel combinations for learning the optimal kernel type, and introduces individual kernel parameters for each variable. The proposed methodology is evaluated with multiple cases from the benchmark Tennessee Eastman Process. The faults are detected for all evaluated cases, including the ones not detected in the original study. |
| 2025-05-02 | SimICD: A Closed-Loop Simulation Framework For ICD Therapy | Hannah Lydon, Milad Kazemi, Martin Bishop et.al. | 2505.01371 | | Accepted for publication in the 47th annual Engineering in Medicine and Biology Conference (EMBC) | Abstract (click to expand)Virtual studies of ICD behaviour are crucial for testing device functionality in a controlled environment prior to clinical application. Although previous works have shown the viability of using in silico testing for diagnosis, there is a notable gap in available models that can simulate therapy progression decisions during arrhythmic episodes. This work introduces SimICD, a simulation tool which combines virtual ICD logic algorithms with cardiac electrophysiology simulations in a feedback loop, allowing the progression of ICD therapy protocols to be simulated for a range of tachy-arrhythmia episodes. Using a cohort of virtual patients, we demonstrate the ability of SimICD to simulate realistic cardiac signals and ICD responses that align with the logic of real-world devices, facilitating the reprogramming of ICD parameters to adapt to specific episodes. |
| 2025-05-02 | Reduced-order structure-property linkages for stochastic metamaterials | Hooman Danesh, Maruthi Annamaraju, Tim Brepols et.al. | 2505.01283 | | | Abstract (click to expand)The capabilities of additive manufacturing have facilitated the design and production of mechanical metamaterials with diverse unit cell geometries. Establishing linkages between the vast design space of unit cells and their effective mechanical properties is critical for the efficient design and performance evaluation of such metamaterials. However, physics-based simulations of metamaterial unit cells across the entire design space are computationally expensive, necessitating a materials informatics framework to efficiently capture complex structure-property relationships. In this work, principal component analysis of 2-point correlation functions is performed to extract the salient features from a large dataset of randomly generated 2D metamaterials. Physics-based simulations are performed using a fast Fourier transform (FFT)-based homogenization approach to efficiently compute the homogenized effective elastic stiffness across the extensive unit cell designs. Subsequently, Gaussian process regression is used to generate reduced-order surrogates, mapping unit cell designs to their homogenized effective elastic constant. It is demonstrated that the adopted workflow enables a high-value low-dimensional representation of the voluminous stochastic metamaterial dataset, facilitating the construction of robust structure-property maps. Finally, an uncertainty-based active learning framework is utilized to train a surrogate model with a significantly smaller number of data points compared to the original full dataset. It is shown that a dataset as small as \(0.61\%\) of the entire dataset is sufficient to generate accurate and robust structure-property maps. |
| 2025-05-02 | Design for a Digital Twin in Clinical Patient Care | Anna-Katharina Nitschke, Carlos Brandl, Fabian Egersdörfer et.al. | 2505.01206 | | submitted to npj Digital Medicine | Abstract (click to expand)Digital Twins hold great potential to personalize clinical patient care, provided the concept is translated to meet specific requirements dictated by established clinical workflows. We present a generalizable Digital Twin design combining knowledge graphs and ensemble learning to reflect the entire patient's clinical journey and assist clinicians in their decision-making. Such Digital Twins can be predictive, modular, evolving, informed, interpretable and explainable with applications ranging from oncology to epidemiology. |
| 2025-05-02 | Transforming physics-informed machine learning to convex optimization | Letian Yi, Siyuan Yang, Ying Cui et.al. | 2505.01047 | link | 41 pages,17 figures | Abstract (click to expand)Physics-Informed Machine Learning (PIML) offers a powerful paradigm of integrating data with physical laws to address important scientific problems, such as parameter estimation, inferring hidden physics, equation discovery, and state prediction, etc. However, PIML still faces many serious optimization challenges that significantly restrict its applications. In this study, we propose a comprehensive framework that transforms PIML to convex optimization to overcome all these limitations, referred to as Convex-PIML. The linear combination of B-splines is utilized to approximate the data, promoting the convexity of the loss function. By replacing the non-convex components of the loss function with convex approximations, the problem is further converted into a sequence of successively refined approximated convex optimization problems. This conversion allows the use of well-established convex optimization algorithms, obtaining solutions effectively and efficiently. Furthermore, an adaptive knot optimization method based on error estimate is introduced to mitigate the spectral bias issue of PIML, further improving the performance. The proposed theoretically guaranteed framework is tested in scenarios with distinct types of physical prior. The results indicate that optimization problems are effectively solved in these scenarios, highlighting the potential of the framework for broad applications. |
| 2025-05-01 | Leveraging Partial SMILES Validation Scheme for Enhanced Drug Design in Reinforcement Learning Frameworks | Xinyu Wang, Jinbo Bi, Minghu Song et.al. | 2505.00530 | | 17 pages, 5 main figures, 2 appendix figures. Submitted to ICML 2025 | Abstract (click to expand)SMILES-based molecule generation has emerged as a powerful approach in drug discovery. Deep reinforcement learning (RL) using large language model (LLM) has been incorporated into the molecule generation process to achieve high matching score in term of likelihood of desired molecule candidates. However, a critical challenge in this approach is catastrophic forgetting during the RL phase, where knowledge such as molecule validity, which often exceeds 99\% during pretraining, significantly deteriorates. Current RL algorithms applied in drug discovery, such as REINVENT, use prior models as anchors to retian pretraining knowledge, but these methods lack robust exploration mechanisms. To address these issues, we propose Partial SMILES Validation-PPO (PSV-PPO), a novel RL algorithm that incorporates real-time partial SMILES validation to prevent catastrophic forgetting while encouraging exploration. Unlike traditional RL approaches that validate molecule structures only after generating entire sequences, PSV-PPO performs stepwise validation at each auto-regressive step, evaluating not only the selected token candidate but also all potential branches stemming from the prior partial sequence. This enables early detection of invalid partial SMILES across all potential paths. As a result, PSV-PPO maintains high validity rates even during aggressive exploration of the vast chemical space. Our experiments on the PMO and GuacaMol benchmark datasets demonstrate that PSV-PPO significantly reduces the number of invalid generated structures while maintaining competitive exploration and optimization performance. While our work primarily focuses on maintaining validity, the framework of PSV-PPO can be extended in future research to incorporate additional forms of valuable domain knowledge, further enhancing reinforcement learning applications in drug discovery. |
| 2025-05-04 | Evaluation of Thermal Control Based on Spatial Thermal Comfort with Reconstructed Environmental Data | Youngkyu Kim, Byounghyun Yoo, Ji Young Yun et.al. | 2505.00468 | | | Abstract (click to expand)Achieving thermal comfort while maintaining energy efficiency is a critical objective in building system control. Conventional thermal comfort models, such as the Predicted Mean Vote (PMV), rely on both environmental and personal variables. However, the use of fixed-location sensors limits the ability to capture spatial variability, which reduces the accuracy of occupant-specific comfort estimation. To address this limitation, this study proposes a new PMV estimation method that incorporates spatial environmental data reconstructed using the Gappy Proper Orthogonal Decomposition (Gappy POD) algorithm. In addition, a group PMV-based control framework is developed to account for the thermal comfort of multiple occupants. The Gappy POD method enables fast and accurate reconstruction of indoor temperature fields from sparse sensor measurements. Using these reconstructed fields and occupant location data, spatially resolved PMV values are calculated. Group-level thermal conditions are then derived through statistical aggregation methods and used to control indoor temperature in a multi-occupant living lab environment. Experimental results show that the Gappy POD algorithm achieves an average relative error below 3\% in temperature reconstruction. PMV distributions varied by up to 1.26 scale units depending on occupant location. Moreover, thermal satisfaction outcomes varied depending on the group PMV method employed. These findings underscore the importance for adaptive thermal control strategies that incorporate both spatial and individual variability, offering valuable insights for future occupant-centric building operations. |
| 2025-05-01 | Subspace-Distance-Enabled Active Learning for Efficient Data-Driven Model Reduction of Parametric Dynamical Systems | Harshit Kapadia, Peter Benner, Lihong Feng et.al. | 2505.00460 | | 31 pages, 10 figures, 4 tables | Abstract (click to expand)In situations where the solution of a high-fidelity dynamical system needs to be evaluated repeatedly, over a vast pool of parametric configurations and in absence of access to the underlying governing equations, data-driven model reduction techniques are preferable. We propose a novel active learning approach to build a parametric data-driven reduced-order model (ROM) by greedily picking the most important parameter samples from the parameter domain. As a result, during the ROM construction phase, the number of high-fidelity solutions dynamically grow in a principled fashion. The high-fidelity solution snapshots are expressed in several parameter-specific linear subspaces, with the help of proper orthogonal decomposition (POD), and the relative distance between these subspaces is used as a guiding mechanism to perform active learning. For successfully achieving this, we provide a distance measure to evaluate the similarity between pairs of linear subspaces with different dimensions, and also show that this distance measure is a metric. The usability of the proposed subspace-distance-enabled active learning (SDE-AL) framework is demonstrated by augmenting two existing non-intrusive reduced-order modeling approaches, and providing their active-learning-driven (ActLearn) extensions, namely, SDE-ActLearn-POD-KSNN, and SDE-ActLearn-POD-NN. Furthermore, we report positive results for two parametric physical models, highlighting the efficiency of the proposed SDE-AL approach. |
| 2025-04-30 | Generative Machine Learning in Adaptive Control of Dynamic Manufacturing Processes: A Review | Suk Ki Lee, Hyunwoong Ko et.al. | 2505.00210 | | 12 pages, 1 figure, 1 table. This paper has been accepted for publication in the proceedings of ASME IDETC-CIE 2025 | Abstract (click to expand)Dynamic manufacturing processes exhibit complex characteristics defined by time-varying parameters, nonlinear behaviors, and uncertainties. These characteristics require sophisticated in-situ monitoring techniques utilizing multimodal sensor data and adaptive control systems that can respond to real-time feedback while maintaining product quality. Recently, generative machine learning (ML) has emerged as a powerful tool for modeling complex distributions and generating synthetic data while handling these manufacturing uncertainties. However, adopting these generative technologies in dynamic manufacturing systems lacks a functional control-oriented perspective to translate their probabilistic understanding into actionable process controls while respecting constraints. This review presents a functional classification of Prediction-Based, Direct Policy, Quality Inference, and Knowledge-Integrated approaches, offering a perspective for understanding existing ML-enhanced control systems and incorporating generative ML. The analysis of generative ML architectures within this framework demonstrates control-relevant properties and potential to extend current ML-enhanced approaches where conventional methods prove insufficient. We show generative ML's potential for manufacturing control through decision-making applications, process guidance, simulation, and digital twins, while identifying critical research gaps: separation between generation and control functions, insufficient physical understanding of manufacturing phenomena, and challenges adapting models from other domains. To address these challenges, we propose future research directions aimed at developing integrated frameworks that combine generative ML and control technologies to address the dynamic complexities of modern manufacturing systems. |
| 2025-04-30 | Generative Multimodal Multiscale Data Fusion for Digital Twins in Aerosol Jet Electronics Printing | Fatemeh Elhambakhsh, Suk Ki Lee, Hyunwoong Ko et.al. | 2505.00176 | | | Abstract (click to expand)The rising demand for high-value electronics necessitates advanced manufacturing techniques capable of meeting stringent specifications for precise, complex, and compact devices, driving the shift toward innovative additive manufacturing (AM) solutions. Aerosol Jet Printing (AJP) is a versatile AM technique that utilizes aerosolized functional materials to accurately print intricate patterns onto diverse substrates. Machine learning (ML)- based Process-Structure-Property (PSP) modeling is essential for enhancing AJP manufacturing, as it quantitatively connects process parameters, structural features, and resulting material properties. However, current ML approaches for modeling PSP relationships in AJP face significant limitations in handling multimodal and multiscale data, underscoring a critical need for generative methods capable of comprehensive analysis through multimodal and multiscale fusion. To address this challenge, this study introduces a novel generative modeling methodology leveraging diffusion models for PSP data fusion in AJP. The proposed method integrates multimodal, multiscale PSP features in two phases: (1) registering the features, and (2) fusing them to generate causal relationships between PSP attributes. A case study demonstrates the registration and fusion of optical microscopy (OM) images and confocal profilometry (CP) data from AJP, along with the fine-tuning of the fusion step. The results effectively capture complex PSP relationships, offering deeper insights into digital twins of dynamic manufacturing systems. |
| 2025-04-30 | Generative AI in Financial Institution: A Global Survey of Opportunities, Threats, and Regulation | Bikash Saha, Nanda Rani, Sandeep Kumar Shukla et.al. | 2504.21574 | | | Abstract (click to expand)Generative Artificial Intelligence (GenAI) is rapidly reshaping the global financial landscape, offering unprecedented opportunities to enhance customer engagement, automate complex workflows, and extract actionable insights from vast financial data. This survey provides an overview of GenAI adoption across the financial ecosystem, examining how banks, insurers, asset managers, and fintech startups worldwide are integrating large language models and other generative tools into their operations. From AI-powered virtual assistants and personalized financial advisory to fraud detection and compliance automation, GenAI is driving innovation across functions. However, this transformation comes with significant cybersecurity and ethical risks. We discuss emerging threats such as AI-generated phishing, deepfake-enabled fraud, and adversarial attacks on AI systems, as well as concerns around bias, opacity, and data misuse. The evolving global regulatory landscape is explored in depth, including initiatives by major financial regulators and international efforts to develop risk-based AI governance. Finally, we propose best practices for secure and responsible adoption - including explainability techniques, adversarial testing, auditability, and human oversight. Drawing from academic literature, industry case studies, and policy frameworks, this chapter offers a perspective on how the financial sector can harness GenAI's transformative potential while navigating the complex risks it introduces. |
| 2025-04-30 | Security Analysis and Implementation of Cryptocurrency Systems on Blockchain 2.0 | Pengfei Gao, Dechao Kong, Xiaoqi Li et.al. | 2504.21367 | | | Abstract (click to expand)Blockchain technology has set off a wave of decentralization in the world since its birth. The trust system constructed by blockchain technology based on cryptography algorithm and computing power provides a practical and powerful solution to solve the trust problem in human society. In order to make more convenient use of the characteristics of blockchain and build applications on it, smart contracts appear. By defining some trigger automatic execution contracts, the application space of blockchain is expanded and the foundation for the rapid development of blockchain is laid. This is blockchain 2.0. However, the programmability of smart contracts also introduces vulnerabilities. In order to cope with the insufficient security guarantee of high-value application networks running on blockchain 2.0 and smart contracts, this article will be represented by Ethereum to introduce the technical details of understanding blockchain 2.0 and the operation principle of contract virtual machines, and explain how cryptocurrencies based on blockchain 2.0 are constructed and operated. The common security problems and solutions are also discussed. Based on relevant research and on-chain practice, this paper provides a complete and comprehensive perspective to understanding cryptocurrency technology based on blockchain 2.0 and provides a reference for building more secure cryptocurrency contracts. |
| 2025-04-30 | Multi-level datasets training method in Physics-Informed Neural Networks | Yao-Hsuan Tsai, Hsiao-Tung Juan, Pao-Hsiung Chiu et.al. | 2504.21328 | | 33 pages, 12figures | Abstract (click to expand)Physics-Informed Neural Networks have emerged as a promising methodology for solving PDEs, gaining significant attention in computer science and various physics-related fields. Despite being demonstrated the ability to incorporate the physics of laws for versatile applications, PINNs still struggle with the challenging problems which are stiff to be solved and/or have high-frequency components in the solutions, resulting in accuracy and convergence issues. It may not only increase computational costs, but also lead to accuracy loss or solution divergence. In this study, an alternative approach is proposed to mitigate the above-mentioned problems. Inspired by the multi-grid method in CFD community, the underlying idea of the current approach is to efficiently remove different frequency errors via training with different levels of training samples, resulting in a simpler way to improve the training accuracy without spending time in fine-tuning of neural network structures, loss weights as well as hyperparameters. To demonstrate the efficacy of current approach, we first investigate canonical 1D ODE with high-frequency component and 2D convection-diffusion equation with V-cycle training strategy. Finally, the current method is employed for the classical benchmark problem of steady Lid-driven cavity flows at different Reynolds numbers, to investigate the applicability and efficacy for the problem involved multiple modes of high and low frequency. By virtue of various training sequence modes, improvement through predictions lead to 30% to 60% accuracy improvement. We also investigate the synergies between current method and transfer learning techniques for more challenging problems (i.e., higher Re). From the present results, it also revealed that the current framework can produce good predictions even for the case of Re=5000, demonstrating the ability to solve complex high-frequency PDEs. |
| 2025-04-30 | Redundancy Analysis and Mitigation for Machine Learning-Based Process Monitoring of Additive Manufacturing | Jiarui Xie, Yaoyao Fiona Zhao et.al. | 2504.21317 | | 13 pages, 5 figures, 2 tables. Accepted by IDETC-CIE 2025 | Abstract (click to expand)The deployment of machine learning (ML)-based process monitoring systems has significantly advanced additive manufacturing (AM) by enabling real-time defect detection, quality assessment, and process optimization. However, redundancy is a critical yet often overlooked challenge in the deployment and operation of ML-based AM process monitoring systems. Excessive redundancy leads to increased equipment costs, compromised model performance, and high computational requirements, posing barriers to industrial adoption. However, existing research lacks a unified definition of redundancy and a systematic framework for its evaluation and mitigation. This paper defines redundancy in ML-based AM process monitoring and categorizes it into sample-level, feature-level, and model-level redundancy. A comprehensive multi-level redundancy mitigation (MLRM) framework is proposed, incorporating advanced methods such as data registration, downscaling, cross-modality knowledge transfer, and model pruning to systematically reduce redundancy while improving model performance. The framework is validated through an ML-based in-situ defect detection case study for directed energy deposition (DED), demonstrating a 91% reduction in latency, a 47% decrease in error rate, and a 99.4% reduction in storage requirements. Additionally, the proposed approach lowers sensor costs and energy consumption, enabling a lightweight, cost-effective, and scalable monitoring system. By defining redundancy and introducing a structured mitigation framework, this study establishes redundancy analysis and mitigation as a key enabler of efficient ML-based process monitoring in production environments. |
| 2025-04-29 | Simulating Heterogeneity within Elastic and Inelastic Discrete Mechanical Models | Jan Raisinger, Qiwei Zhang, John E. Bolander et.al. | 2504.20861 | | 24 pages, 11 figures | Abstract (click to expand)The study investigates the elastic and fracture behaviors of discrete, elastically homogeneous models of heterogeneous media. The homogeneity is accomplished either by volumetric-deviatoric decomposition of constitutive function or by an auxiliary stress homogenization method. The elastic parameters of the homogenized material models are randomly varied in space to introduce heterogeneity independently of the geometric properties of the discrete model. Several forms of randomization are investigated using statistical properties of nodal stress oscillations in periodic representative volume elements (RVEs). It is found that the stress oscillations present in discrete models built on heterogeneous geometric structures with standard constitutive models cannot be replicated by randomization of the elastically homogeneous discrete system. The marginal distributions as well as dependencies between stress tensor components cannot be adequately matched. With respect to quasi-brittle fracture behavior, the macroscopic response of the different models is studied for the load case of uniaxial tension. The elastically homogenized material provides higher peak stress occurring at lower strain levels and a steeper softening phase, compared to the standard material. Randomization of the elastic material parameters, as well as adjustment of inelastic material parameters, brings the macroscopic response of the homogenized material close to that of the standard material, although the damage distribution prior to the strain localization differs. These findings provide insight into the potential for controlled, random assignment of heterogeneity in homogeneous models, using physically-based discretizations of material structure with standard constitutive models for comparison. |
| 2025-04-29 | DiffLiB: High-fidelity differentiable modeling of lithium-ion batteries and efficient gradient-based parameter identification | Weipeng Xu, Kaiqi Yang, Yuzhi Zhang et.al. | 2504.20674 | | | Abstract (click to expand)The physics-based Doyle-Fuller-Newman (DFN) model, widely adopted for its precise electrochemical modeling, stands out among various simulation models of lithium-ion batteries (LIBs). Although the DFN model is powerful in forward predictive analysis, the inverse identification of its model parameters has remained a long-standing challenge. The numerous unknown parameters associated with the nonlinear, time-dependent, and multi-scale DFN model are extremely difficult to be determined accurately and efficiently, hindering the practical use of such battery simulation models in industrial applications. To tackle this challenge, we introduce DiffLiB, a high-fidelity finite-element-based LIB simulation framework, equipped with advanced differentiable programming techniques so that efficient gradient-based inverse parameter identification is enabled. Customized automatic differentiation rules are defined by identifying the VJP (vector-Jacobian product) structure in the chain rule and implemented using adjoint-based implicit differentiation methods. Four numerical examples, including both 2D and 3D forward predictions and inverse parameter identification, are presented to validate the accuracy and computational efficiency of DiffLiB. Benchmarking against COMSOL demonstrates excellent agreement in forward predictions, with terminal voltage discrepancies maintaining a root-mean-square error (RMSE) below 2 mV across all test conditions. In parameter identification tasks using experimentally measured voltage data, the proposed gradient-based optimization scheme achieves superior computational performance, with 96% fewer forward predictions and 72% less computational time compared with gradient-free approaches. These results demonstrate that DiffLiB is a versatile and powerful computational framework for the development of advanced LIBs. |
| 2025-04-29 | Faster Random Walk-based Capacitance Extraction with Generalized Antithetic Sampling | Periklis Liaskovitis, Marios Visvardis, Efthymios Efstathiou et.al. | 2504.20586 | | | Abstract (click to expand)Floating random walk-based capacitance extraction has emerged in recent years as a tried and true approach for extracting parasitic capacitance in very large scale integrated circuits. Being a Monte Carlo method, its performance is dependent on the variance of sampled quantities and variance reduction methods are crucial for the challenges posed by ever denser process technologies and layout-dependent effects. In this work, we present a novel, universal variance reduction method for floating random walk-based capacitance extraction, which is conceptually simple, highly efficient and provably reduces variance in all extractions, especially when layout-dependent effects are present. It is complementary to existing mathematical formulations for variance reduction and its performance gains are experienced on top of theirs. Numerical experiments demonstrate substantial such gains of up to 30% in number of walks necessary and even more in actual extraction times compared to the best previously proposed variance reduction approaches for the floating random-walk. |
| 2025-04-28 | Multiscale modelling of thermally stressed superelastic polyimide | Jerome Samuel S, Puneet Kumar Patra, Md Rushdie Ibne Islam et.al. | 2504.20123 | | 25 pages, 17 figures | Abstract (click to expand)Many thermo-mechanical processes, such as thermal expansion and stress relaxation, originate at the atomistic scale. We develop a sequential multiscale approach to study thermally stressed superelastic polyimide to explore these effects. The continuum-scale smoothed particle hydrodynamics (SPH) model is coupled with atomistic molecular dynamics (MD) through constitutive modelling, where thermo-mechanical properties and equations of state are derived from MD simulations. The results are verified through benchmark problems of heat transfer. Finally, we analyse the insulating capabilities of superelastic polyimide by simulating the thermal response of an aluminium plate. The result shows a considerable reduction in the thermal stress, strain and temperature field development in the aluminium plate when superelastic polyimide is used as an insulator. The present work demonstrates the effectiveness of the multi-scale method in capturing thermo-mechanical interactions in superelastic polyimide. |
| 2025-04-28 | Stochastic Subspace via Probabilistic Principal Component Analysis for Characterizing Model Error | Akash Yadav, Ruda Zhang et.al. | 2504.19963 | | | Abstract (click to expand)This paper proposes a probabilistic model of subspaces based on the probabilistic principal component analysis (PCA). Given a sample of vectors in the embedding space -- commonly known as a snapshot matrix -- this method uses quantities derived from the probabilistic PCA to construct distributions of the sample matrix, as well as the principal subspaces. It is applicable to projection-based reduced-order modeling methods, such as proper orthogonal decomposition and related model reduction methods. The stochastic subspace thus constructed can be used, for example, to characterize model-form uncertainty in computational mechanics. The proposed method has multiple desirable properties: (1) it is naturally justified by the probabilistic PCA and has analytic forms for the induced random matrix models; (2) it satisfies linear constraints, such as boundary conditions of all kinds, by default; (3) it has only one hyperparameter, which significantly simplifies training; and (4) its algorithm is very easy to implement. We compare the proposed method with existing approaches in a low-dimensional visualization example and a parametric static problem, and demonstrate its performance in a dynamics model of a space structure. |
| 2025-04-26 | Spreading of highly cohesive metal powders with transverse oscillation kinematics | Reimar Weissbach, Garrett Adams, Patrick M. Praegla et.al. | 2504.18981 | | | Abstract (click to expand)Powder bed additive manufacturing processes such as laser powder bed fusion (LPBF) or binder jetting (BJ) benefit from using fine (D50 \(\leq20~\mu m\) ) powders. However, the increasing level of cohesion with decreasing particle size makes spreading a uniform and continuous layer challenging. As a result, LPBF typically employs a coarser size distribution, and rotating roller mechanisms are used in BJ machines, that can create wave-like surface profiles due to roller run-out. In this work, a transverse oscillation kinematic for powder spreading is proposed, explored computationally, and validated experimentally. Simulations are performed using an integrated discrete element-finite element (DEM-FEM) framework and predict that transverse oscillation of a non-rotating roller facilitates the spreading of dense powder layers (beyond 50% packing fraction) with a high level of robustness to kinematic parameters. The experimental study utilizes a custom-built mechanized powder spreading testbed and X-ray transmission imaging for the analysis of spread powder layers. Experimental results generally validate the computational results, however, also exhibit parasitic layer cracking. For transverse oscillation frequencies above 200 Hz, powder layers of high packing fraction (between 50-60%) were formed, and for increased layer thicknesses, highly uniform and continuous layers were deposited. Statistical analysis of the experimental powder layer morphology as a function of kinematic spreading parameters revealed that an increasing transverse surface velocity improves layer uniformity and reduces cracking defects. This suggests that with minor improvements to the machine design, the proposed transverse oscillation kinematic has the potential to result in thin and consistently uniform powder layers of highly cohesive powder. |
| 2025-04-26 | Scientific Open-Source Software Is Less Likely to Become Abandoned Than One Might Think! Lessons from Curating a Catalog of Maintained Scientific Software | Addi Malviya Thakur, Reed Milewicz, Mahmoud Jahanshahi et.al. | 2504.18971 | | | Abstract (click to expand)Scientific software is essential to scientific innovation and in many ways it is distinct from other types of software. Abandoned (or unmaintained), buggy, and hard to use software, a perception often associated with scientific software can hinder scientific progress, yet, in contrast to other types of software, its longevity is poorly understood. Existing data curation efforts are fragmented by science domain and/or are small in scale and lack key attributes. We use large language models to classify public software repositories in World of Code into distinct scientific domains and layers of the software stack, curating a large and diverse collection of over 18,000 scientific software projects. Using this data, we estimate survival models to understand how the domain, infrastructural layer, and other attributes of scientific software affect its longevity. We further obtain a matched sample of non-scientific software repositories and investigate the differences. We find that infrastructural layers, downstream dependencies, mentions of publications, and participants from government are associated with a longer lifespan, while newer projects with participants from academia had shorter lifespan. Against common expectations, scientific projects have a longer lifetime than matched non-scientific open-source software projects. We expect our curated attribute-rich collection to support future research on scientific software and provide insights that may help extend longevity of both scientific and other projects. |
| 2025-04-30 | 4DGS-CC: A Contextual Coding Framework for 4D Gaussian Splatting Data Compression | Zicong Chen, Zhenghao Chen, Wei Jiang et.al. | 2504.18925 | | | Abstract (click to expand)Storage is a significant challenge in reconstructing dynamic scenes with 4D Gaussian Splatting (4DGS) data. In this work, we introduce 4DGS-CC, a contextual coding framework that compresses 4DGS data to meet specific storage constraints. Building upon the established deformable 3D Gaussian Splatting (3DGS) method, our approach decomposes 4DGS data into 4D neural voxels and a canonical 3DGS component, which are then compressed using Neural Voxel Contextual Coding (NVCC) and Vector Quantization Contextual Coding (VQCC), respectively. Specifically, we first decompose the 4D neural voxels into distinct quantized features by separating the temporal and spatial dimensions. To losslessly compress each quantized feature, we leverage the previously compressed features from the temporal and spatial dimensions as priors and apply NVCC to generate the spatiotemporal context for contextual coding. Next, we employ a codebook to store spherical harmonics information from canonical 3DGS as quantized vectors, which are then losslessly compressed by using VQCC with the auxiliary learned hyperpriors for contextual coding, thereby reducing redundancy within the codebook. By integrating NVCC and VQCC, our contextual coding framework, 4DGS-CC, enables multi-rate 4DGS data compression tailored to specific storage requirements. Extensive experiments on three 4DGS data compression benchmarks demonstrate that our method achieves an average storage reduction of approximately 12 times while maintaining rendering fidelity compared to our baseline 4DGS approach. |
| 2025-04-24 | QuantBench: Benchmarking AI Methods for Quantitative Investment | Saizhuo Wang, Hao Kong, Jiadong Guo et.al. | 2504.18600 | | | Abstract (click to expand)The field of artificial intelligence (AI) in quantitative investment has seen significant advancements, yet it lacks a standardized benchmark aligned with industry practices. This gap hinders research progress and limits the practical application of academic innovations. We present QuantBench, an industrial-grade benchmark platform designed to address this critical need. QuantBench offers three key strengths: (1) standardization that aligns with quantitative investment industry practices, (2) flexibility to integrate various AI algorithms, and (3) full-pipeline coverage of the entire quantitative investment process. Our empirical studies using QuantBench reveal some critical research directions, including the need for continual learning to address distribution shifts, improved methods for modeling relational financial data, and more robust approaches to mitigate overfitting in low signal-to-noise environments. By providing a common ground for evaluation and fostering collaboration between researchers and practitioners, QuantBench aims to accelerate progress in AI for quantitative investment, similar to the impact of benchmark platforms in computer vision and natural language processing. |
| 2025-04-25 | Discovering Governing Equations of Geomagnetic Storm Dynamics with Symbolic Regression | Stefano Markidis, Jonah Ekelund, Luca Pennati et.al. | 2504.18461 | | Accepted for publication in the 25th International Conference on Computational Science proceedings | Abstract (click to expand)Geomagnetic storms are large-scale disturbances of the Earth's magnetosphere driven by solar wind interactions, posing significant risks to space-based and ground-based infrastructure. The Disturbance Storm Time (Dst) index quantifies geomagnetic storm intensity by measuring global magnetic field variations. This study applies symbolic regression to derive data-driven equations describing the temporal evolution of the Dst index. We use historical data from the NASA OMNIweb database, including solar wind density, bulk velocity, convective electric field, dynamic pressure, and magnetic pressure. The PySR framework, an evolutionary algorithm-based symbolic regression library, is used to identify mathematical expressions linking dDst/dt to key solar wind. The resulting models include a hierarchy of complexity levels and enable a comparison with well-established empirical models such as the Burton-McPherron-Russell and O'Brien-McPherron models. The best-performing symbolic regression models demonstrate superior accuracy in most cases, particularly during moderate geomagnetic storms, while maintaining physical interpretability. Performance evaluation on historical storm events includes the 2003 Halloween Storm, the 2015 St. Patrick's Day Storm, and a 2017 moderate storm. The results provide interpretable, closed-form expressions that capture nonlinear dependencies and thresholding effects in Dst evolution. |
| 2025-04-25 | A Composable Game-Theoretic Framework for Blockchains | Zeta Avarikioti, Georg Fuchsbauer, Pim Keer et.al. | 2504.18214 | | 19 pages (12 for main paper), 5 figures | Abstract (click to expand)Blockchains rely on economic incentives to ensure secure and decentralised operation, making incentive compatibility a core design concern. However, protocols are rarely deployed in isolation. Applications interact with the underlying consensus and network layers, and multiple protocols may run concurrently on the same chain. These interactions give rise to complex incentive dynamics that traditional, isolated analyses often fail to capture. We propose the first compositional game-theoretic framework for blockchain protocols. Our model represents blockchain protocols as interacting games across layers -- application, network, and consensus. It enables formal reasoning about incentive compatibility under composition by introducing two key abstractions: the cross-layer game, which models how strategies in one layer influence others, and cross-application composition, which captures how application protocols interact concurrently through shared infrastructure. We illustrate our framework through case studies on HTLCs, Layer-2 protocols, and MEV, showing how compositional analysis reveals subtle incentive vulnerabilities and supports modular security proofs. |
| 2025-04-25 | A Machine Learning Approach For Bitcoin Forecasting | Stefano Sossi-Rojas, Gissel Velarde, Damian Zieba et.al. | 2504.18206 | | 15 pages | Abstract (click to expand)Bitcoin is one of the cryptocurrencies that is gaining more popularity in recent years. Previous studies have shown that closing price alone is not enough to forecast stock market series. We introduce a new set of time series and demonstrate that a subset is necessary to improve directional accuracy based on a machine learning ensemble. In our experiments, we study which time series and machine learning algorithms deliver the best results. We found that the most relevant time series that contribute to improving directional accuracy are Open, High and Low, with the largest contribution of Low in combination with an ensemble of Gated Recurrent Unit network and a baseline forecast. The relevance of other Bitcoin-related features that are not price-related is negligible. The proposed method delivers similar performance to the state-of-the-art when observing directional accuracy. |
| 2025-04-25 | SMARTFinRAG: Interactive Modularized Financial RAG Benchmark | Yiwei Zha et.al. | 2504.18024 | link | For open source github repo, see https://github.com/JonathanZha47/SMARTFinRAG | Abstract (click to expand)Financial sectors are rapidly adopting language model technologies, yet evaluating specialized RAG systems in this domain remains challenging. This paper introduces SMARTFinRAG, addressing three critical gaps in financial RAG assessment: (1) a fully modular architecture where components can be dynamically interchanged during runtime; (2) a document-centric evaluation paradigm generating domain-specific QA pairs from newly ingested financial documents; and (3) an intuitive interface bridging research-implementation divides. Our evaluation quantifies both retrieval efficacy and response quality, revealing significant performance variations across configurations. The platform's open-source architecture supports transparent, reproducible research while addressing practical deployment challenges faced by financial institutions implementing RAG systems. |
| 2025-04-24 | EAQGA: A Quantum-Enhanced Genetic Algorithm with Novel Entanglement-Aware Crossovers | Mohammad Kashfi Haghighi, Matthieu Fortin-Deschênes, Christophe Pere et.al. | 2504.17923 | | This work has been submitted to the IEEE (QCE25) for possible publication | Abstract (click to expand)Genetic algorithms are highly effective optimization techniques for many computationally challenging problems, including combinatorial optimization tasks like portfolio optimization. Quantum computing has also shown potential in addressing these complex challenges. Combining these approaches, quantum genetic algorithms leverage the principles of superposition and entanglement to enhance the performance of classical genetic algorithms. In this work, we propose a novel quantum genetic algorithm introducing an innovative crossover strategy to generate quantum circuits from a binary solution. We incorporate a heuristic method to encode entanglement patterns from parent solutions into circuits for the next generation. Our algorithm advances quantum genetic algorithms by utilizing a limited number of entanglements, enabling efficient exploration of optimal solutions without significantly increasing circuit depth, making it suitable for near-term applications. We test this approach on a portfolio optimization problem using an IBM 127 qubits Eagle processor (ibm_quebec) and simulators. Compared to state-of-the-art algorithms, our results show that the proposed method improves fitness values by 33.6% over classical genetic algorithm and 37.2% over quantum-inspired genetic algorithm, using the same iteration counts and population sizes with real quantum hardware employing 100 qubits. These findings highlight the potential of current quantum computers to address real-world utility-scale combinatorial optimization problems. |
| 2025-04-24 | polyGen: A Learning Framework for Atomic-level Polymer Structure Generation | Ayush Jain, Rampi Ramprasad et.al. | 2504.17656 | | | Abstract (click to expand)Synthetic polymeric materials underpin fundamental technologies in the energy, electronics, consumer goods, and medical sectors, yet their development still suffers from prolonged design timelines. Although polymer informatics tools have supported speedup, polymer simulation protocols continue to face significant challenges: on-demand generation of realistic 3D atomic structures that respect the conformational diversity of polymer structures. Generative algorithms for 3D structures of inorganic crystals, bio-polymers, and small molecules exist, but have not addressed synthetic polymers. In this work, we introduce polyGen, the first latent diffusion model designed specifically to generate realistic polymer structures from minimal inputs such as the repeat unit chemistry alone, leveraging a molecular encoding that captures polymer connectivity throughout the architecture. Due to a scarce dataset of only 3855 DFT-optimized polymer structures, we augment our training with DFT-optimized molecular structures, showing improvement in joint learning between similar chemical structures. We also establish structure matching criteria to benchmark our approach on this novel problem. polyGen effectively generates diverse conformations of both linear chains and complex branched structures, though its performance decreases when handling repeat units with a high atom count. Given these initial results, polyGen represents a paradigm shift in atomic-level structure generation for polymer science-the first proof-of-concept for predicting realistic atomic-level polymer conformations while accounting for their intrinsic structural flexibility. |
| 2025-04-24 | Towards Equitable Rail Service Allocation Through Fairness-Oriented Timetabling in Liberalized Markets | David Muñoz-Valero, Juan Moreno-Garcia, Julio Alberto López-Gómez et.al. | 2504.17489 | | 30 pages, 7 figures | Abstract (click to expand)Over the last few decades, European rail transport has undergone major changes as part of the process of liberalization set out in European regulations. In this context of liberalization, railway undertakings compete with each other for the limited infrastructure capacity available to offer their rail services. The infrastructure manager is responsible for the equitable allocation of infrastructure between all companies in the market, which is essential to ensure the efficiency and sustainability of this competitive ecosystem. In this paper, a methodology based on Jain, Gini and Atkinson equity metrics is used to solve the rail service allocation problem in a liberalized railway market, analyzing the solutions obtained. The results show that the proposed methodology and the equity metrics used allow for equitable planning in different competitiveness scenarios. These results contrast with solutions where the objective of the infrastructure manager is to maximize its own profit, without regard for the equitable allocation of infrastructure. Therefore, the computational tests support the methodology and metrics used as a planning and decision support tool in a liberalized railway market. |
| 2025-04-24 | An approach based on metaheuristic algorithms to the timetabling problem in deregulated railway markets | David Muñoz-Valero, Juan Moreno-Garcia, Julio Alberto López-Gómez et.al. | 2504.17455 | | 20 pages, 16 figures | Abstract (click to expand)The train timetabling problem in liberalized railway markets represents a challenge to the coordination between infrastructure managers and railway undertakings. Efficient scheduling is critical in maximizing infrastructure capacity and utilization while adhering as closely as possible to the requests of railway undertakings. These objectives ultimately contribute to maximizing the infrastructure manager's revenues. This paper sets out a modular simulation framework to reproduce the dynamics of deregulated railway systems. Ten metaheuristic algorithms using the MEALPY Python library are then evaluated in order to optimize train schedules in the liberalized Spanish railway market. The results show that the Genetic Algorithm outperforms others in revenue optimization, convergence speed, and schedule adherence. Alternatives, such as Particle Swarm Optimization and Ant Colony Optimization Continuous, show slower convergence and higher variability. The results emphasize the trade-off between scheduling more trains and adhering to requested times, providing insights into solving complex scheduling problems in deregulated railway systems. |
| 2025-04-24 | Detection, Classification and Prevalence of Self-Admitted Aging Debt | Murali Sridharan, Mika Mäntylä, Leevi Rantala et.al. | 2504.17428 | | Draft | Abstract (click to expand)Context: Previous research on software aging is limited with focus on dynamic runtime indicators like memory and performance, often neglecting evolutionary indicators like source code comments and narrowly examining legacy issues within the TD context. Objective: We introduce the concept of Aging Debt (AD), representing the increased maintenance efforts and costs needed to keep software updated. We study AD through Self-Admitted Aging Debt (SAAD) observed in source code comments left by software developers. Method: We employ a mixed-methods approach, combining qualitative and quantitative analyses to detect and measure AD in software. This includes framing SAAD patterns from the source code comments after analysing the source code context, then utilizing the SAAD patterns to detect SAAD comments. In the process, we develop a taxonomy for SAAD that reflects the temporal aging of software and its associated debt. Then we utilize the taxonomy to quantify the different types of AD prevalent in OSS repositories. Results: Our proposed taxonomy categorizes temporal software aging into Active and Dormant types. Our extensive analysis of over 9,000+ Open Source Software (OSS) repositories reveals that more than 21% repositories exhibit signs of SAAD as observed from our gold standard SAAD dataset. Notably, Dormant AD emerges as the predominant category, highlighting a critical but often overlooked aspect of software maintenance. Conclusion: As software volume grows annually, so do evolutionary aging and maintenance challenges; our proposed taxonomy can aid researchers in detailed software aging studies and help practitioners develop improved and proactive maintenance strategies. |
| 2025-04-24 | Data-Driven Surrogate Modeling Techniques to Predict the Effective Contact Area of Rough Surface Contact Problems | Tarik Sahin, Jacopo Bonari, Sebastian Brandstaeter et.al. | 2504.17354 | | | Abstract (click to expand)The effective contact area in rough surface contact plays a critical role in multi-physics phenomena such as wear, sealing, and thermal or electrical conduction. Although accurate numerical methods, like the Boundary Element Method (BEM), are available to compute this quantity, their high computational cost limits their applicability in multi-query contexts, such as uncertainty quantification, parameter identification, and multi-scale algorithms, where many repeated evaluations are required. This study proposes a surrogate modeling framework for predicting the effective contact area using fast-to-evaluate data-driven techniques. Various machine learning algorithms are trained on a precomputed dataset, where the inputs are the imposed load and statistical roughness parameters, and the output is the corresponding effective contact area. All models undergo hyperparameter optimization to enable fair comparisons in terms of predictive accuracy and computational efficiency, evaluated using established quantitative metrics. Among the models, the Kernel Ridge Regressor demonstrates the best trade-off between accuracy and efficiency, achieving high predictive accuracy, low prediction time, and minimal training overhead-making it a strong candidate for general-purpose surrogate modeling. The Gaussian Process Regressor provides an attractive alternative when uncertainty quantification is required, although it incurs additional computational cost due to variance estimation. The generalization capability of the Kernel Ridge model is validated on an unseen simulation scenario, confirming its ability to transfer to new configurations. Database generation constitutes the dominant cost in the surrogate modeling process. Nevertheless, the approach proves practical and efficient for multi-query tasks, even when accounting for this initial expense. |
| 2025-04-24 | Tokenizing Stock Prices for Enhanced Multi-Step Forecast and Prediction | Zhuohang Zhu, Haodong Chen, Qiang Qu et.al. | 2504.17313 | | | Abstract (click to expand)Effective stock price forecasting (estimating future prices) and prediction (estimating future price changes) are pivotal for investors, regulatory agencies, and policymakers. These tasks enable informed decision-making, risk management, strategic planning, and superior portfolio returns. Despite their importance, forecasting and prediction are challenging due to the dynamic nature of stock price data, which exhibit significant temporal variations in distribution and statistical properties. Additionally, while both forecasting and prediction targets are derived from the same dataset, their statistical characteristics differ significantly. Forecasting targets typically follow a log-normal distribution, characterized by significant shifts in mean and variance over time, whereas prediction targets adhere to a normal distribution. Furthermore, although multi-step forecasting and prediction offer a broader perspective and richer information compared to single-step approaches, it is much more challenging due to factors such as cumulative errors and long-term temporal variance. As a result, many previous works have tackled either single-step stock price forecasting or prediction instead. To address these issues, we introduce a novel model, termed Patched Channel Integration Encoder (PCIE), to tackle both stock price forecasting and prediction. In this model, we utilize multiple stock channels that cover both historical prices and price changes, and design a novel tokenization method to effectively embed these channels in a cross-channel and temporally efficient manner. Specifically, the tokenization process involves univariate patching and temporal learning with a channel-mixing encoder to reduce cumulative errors. Comprehensive experiments validate that PCIE outperforms current state-of-the-art models in forecast and prediction tasks. |
| 2025-04-23 | Reinforcement learning framework for the mechanical design of microelectronic components under multiphysics constraints | Siddharth Nair, Timothy F. Walsh, Greg Pickrell et.al. | 2504.17142 | | 27 pages of main text, 15 figures | Abstract (click to expand)This study focuses on the development of reinforcement learning based techniques for the design of microelectronic components under multiphysics constraints. While traditional design approaches based on global optimization approaches are effective when dealing with a small number of design parameters, as the complexity of the solution space and of the constraints increases different techniques are needed. This is an important reason that makes the design and optimization of microelectronic components (characterized by large solution space and multiphysics constraints) very challenging for traditional methods. By taking as prototypical elements an application-specific integrated circuit (ASIC) and a heterogeneously integrated (HI) interposer, we develop and numerically test an optimization framework based on reinforcement learning (RL). More specifically, we consider the optimization of the bonded interconnect geometry for an ASIC chip as well as the placement of components on a HI interposer while satisfying thermoelastic and design constraints. This placement problem is particularly interesting because it features a high-dimensional solution space. |
| 2025-04-23 | Demonstration of an AI-driven workflow for dynamic x-ray spectroscopy | Ming Du, Mark Wolfman, Chengjun Sun et.al. | 2504.17124 | | | Abstract (click to expand)X-ray absorption near edge structure (XANES) spectroscopy is a powerful technique for characterizing the chemical state and symmetry of individual elements within materials, but requires collecting data at many energy points which can be time-consuming. While adaptive sampling methods exist for efficiently collecting spectroscopic data, they often lack domain-specific knowledge about XANES spectra structure. Here we demonstrate a knowledge-injected Bayesian optimization approach for adaptive XANES data collection that incorporates understanding of spectral features like absorption edges and pre-edge peaks. We show this method accurately reconstructs the absorption edge of XANES spectra using only 15-20% of the measurement points typically needed for conventional sampling, while maintaining the ability to determine the x-ray energy of the sharp peak after absorption edge with errors less than 0.03 eV, the absorption edge with errors less than 0.1 eV; and overall root-mean-square errors less than 0.005 compared to compared to traditionally sampled spectra. Our experiments on battery materials and catalysts demonstrate the method's effectiveness for both static and dynamic XANES measurements, improving data collection efficiency and enabling better time resolution for tracking chemical changes. This approach advances the degree of automation in XANES experiments reducing the common errors of under- or over-sampling points in near the absorption edge and enabling dynamic experiments that require high temporal resolution or limited measurement time. |
| 2025-04-23 | An Entropy Stable Formulation of Two-equation Turbulence Models with Particular Reference to the k-epsilon Model | Guillermo Hauke, Thomas J. R. Hughes et.al. | 2504.17110 | | 50 pages, 13 figures | Abstract (click to expand)Consistency and stability are two essential ingredients in the design of numerical algorithms for partial differential equations. Robust algorithms can be developed by incorporating nonlinear physical stability principles in their design, such as the entropy production inequality (i.e., the Clausius-Duhem inequality or second law of thermodynamics), rather than by simply adding artificial viscosity (a common approach). This idea is applied to the k-epsilon and two-equation turbulence models by introducing space-time averaging. Then, a set of entropy variables can be defined which leads to a symmetric system of advective-diffusive equations. Positivity and symmetry of the equations require certain constraints on the turbulence diffusivity coefficients and the turbulence source terms. With these, we are able to design entropy producing two-equation turbulence models and, in particular, the k-epsilon model. |
| 2025-04-23 | Mixing Data-Driven and Physics-Based Constitutive Models using Uncertainty-Driven Phase Fields | J. Storm, W. Sun, I. B. C. M. Rocha et.al. | 2504.16713 | | | Abstract (click to expand)There is a high interest in accelerating multiscale models using data-driven surrogate modeling techniques. Creating a large training dataset encompassing all relevant load scenarios is essential for a good surrogate, yet the computational cost of producing this data quickly becomes a limiting factor. Commonly, a pre-trained surrogate is used throughout the computational domain. Here, we introduce an alternative adaptive mixture approach that uses a fast probabilistic surrogate model as constitutive model when possible, but resorts back to the true high-fidelity model when necessary. The surrogate is thus not required to be accurate for every possible load condition, enabling a significant reduction in the data collection time. We achieve this by creating phases in the computational domain corresponding to the different models. These phases evolve using a phase-field model driven by the surrogate uncertainty. When the surrogate uncertainty becomes large, the phase-field model causes a local transition from the surrogate to the high-fidelity model, maintaining a highly accurate simulation. We discuss the requirements of this approach to achieve accurate and numerically stable results and compare the phase-field model to a purely local approach that does not enforce spatial smoothness for the phase mixing. Using a Gaussian Process surrogate for an elasto-plastic material, we demonstrate the potential of this mixture of models to accelerate multiscale simulations. |
| 2025-04-23 | 3D-1D modelling of cranial plate heating induced by low or medium frequency magnetic fields | Alessandro Arduino, Oriano Bottauscio, Denise Grappein et.al. | 2504.16600 | | | Abstract (click to expand)Safety assessment of patients with one-dimensionally structured passive implants, like cranial plates or stents, exposed to low or medium frequency magnetic fields, like those generated in magnetic resonance imaging or magnetic hyperthermia, can be challenging, because of the different length scales of the implant and the human body. Most of the methods used to estimate the heating induced near such implants neglect the presence of the metallic materials within the body, modeling the metal as thermal seeds. To overcome this limitation, a novel numerical approach that solves three-dimensional and one-dimensional coupled problems is proposed. This method leads to improved results by modelling the thermal diffusion through the highly conductive metallic implants. A comparison of the proposed method predictions with measurements performed on a cranial plate exposed to the magnetic field generated by a gradient coil system for magnetic resonance imaging is presented, showing an improved accuracy up to 25 % with respect to the method based on thermal seeds. The proposed method is finally applied to a magnetic hyperthermia case study in which a patient with a cranial plate is exposed to the magnetic field generated by a collar-type magnetic hyperthermia applicator for neck tumour treatment, predicting a temperature increase in proximity of the implant that is 10 % lower than the one overestimated by relying on thermal seeds. |
| 2025-04-23 | Preconditioning Natural and Second Order Gradient Descent in Quantum Optimization: A Performance Benchmark | Théo Lisart-Liebermann, Arcesio Castañeda Medina et.al. | 2504.16518 | | 17 pages, 36 figures, in review | Abstract (click to expand)The optimization of parametric quantum circuits is technically hindered by three major obstacles: the non-convex nature of the objective function, noisy gradient evaluations, and the presence of barren plateaus. As a result, the selection of classical optimizer becomes a critical factor in assessing and exploiting quantum-classical applications. One promising approach to tackle these challenges involves incorporating curvature information into the parameter update. The most prominent methods in this field are quasi-Newton and quantum natural gradient methods, which can facilitate faster convergence compared to first-order approaches. Second order methods however exhibit a significant trade-off between computational cost and accuracy, as well as heightened sensitivity to noise. This study evaluates the performance of three families of optimizers on synthetically generated MaxCut problems on a shallow QAOA algorithm. To address noise sensitivity and iteration cost, we demonstrate that incorporating secant-penalization in the BFGS update rule (SP-BFGS) yields improved outcomes for QAOA optimization problems, introducing a novel approach to stabilizing BFGS updates against gradient noise. |
| 2025-04-23 | Comparing Different Transformer Model Structures for Stock Prediction | Qizhao Chen et.al. | 2504.16361 | | | Abstract (click to expand)This paper compares different Transformer model architectures for stock index prediction. While many studies have shown that Transformers perform well in stock price forecasting, few have explored how different structural designs impact performance. Most existing works treat the Transformer as a black box, overlooking how specific architectural choices may affect predictive accuracy. However, understanding these differences is critical for developing more effective forecasting models. This study aims to identify which Transformer variant is most suitable for stock forecasting. This study evaluates five Transformer structures: (1) encoder-only Transformer, (2) decoder-only Transformer, (3) Vanilla Transformer (encoder + decoder), (4) Vanilla Transformer without embedding layers, and (5) Vanilla Transformer with ProbSparse attention. Results show that Transformer-based models generally outperform traditional approaches. Transformer with decoder only structure outperforms all other models in all scenarios. Transformer with ProbSparse attention has the worst performance in almost all cases. |
| 2025-04-22 | Quantum Doubly Stochastic Transformers | Jannis Born, Filip Skogh, Kahn Rhrissorrakrai et.al. | 2504.16275 | | Under Review | Abstract (click to expand)At the core of the Transformer, the Softmax normalizes the attention matrix to be right stochastic. Previous research has shown that this often destabilizes training and that enforcing the attention matrix to be doubly stochastic (through Sinkhorn's algorithm) consistently improves performance across different tasks, domains and Transformer flavors. However, Sinkhorn's algorithm is iterative, approximative, non-parametric and thus inflexible w.r.t. the obtained doubly stochastic matrix (DSM). Recently, it has been proven that DSMs can be obtained with a parametric quantum circuit, yielding a novel quantum inductive bias for DSMs with no known classical analogue. Motivated by this, we demonstrate the feasibility of a hybrid classical-quantum doubly stochastic Transformer (QDSFormer) that replaces the Softmax in the self-attention layer with a variational quantum circuit. We study the expressive power of the circuit and find that it yields more diverse DSMs that better preserve information than classical operators. Across multiple small-scale object recognition tasks, we find that our QDSFormer consistently surpasses both a standard Vision Transformer and other doubly stochastic Transformers. Beyond the established Sinkformer, this comparison includes a novel quantum-inspired doubly stochastic Transformer (based on QR decomposition) that can be of independent interest. The QDSFormer also shows improved training stability and lower performance variation suggesting that it may mitigate the notoriously unstable training of ViTs on small-scale data. |
| 2025-04-22 | Accurate and generalizable protein-ligand binding affinity prediction with geometric deep learning | Krinos Li, Xianglu Xiao, Zijun Zhong et.al. | 2504.16261 | | 6 pages, 5 figures | Abstract (click to expand)Protein-ligand binding complexes are ubiquitous and essential to life. Protein-ligand binding affinity prediction (PLA) quantifies the binding strength between ligands and proteins, providing crucial insights for discovering and designing potential candidate ligands. While recent advances have been made in predicting protein-ligand complex structures, existing algorithms for interaction and affinity prediction suffer from a sharp decline in performance when handling ligands bound with novel unseen proteins. We propose IPBind, a geometric deep learning-based computational method, enabling robust predictions by leveraging interatomic potential between complex's bound and unbound status. Experimental results on widely used binding affinity prediction benchmarks demonstrate the effectiveness and universality of IPBind. Meanwhile, it provides atom-level insights into prediction. This work highlights the advantage of leveraging machine learning interatomic potential for predicting protein-ligand binding affinity. |
| 2025-04-22 | A Systematic Literature Review of Software Engineering Research on Jupyter Notebook | Md Saeed Siddik, Hao Li, Cor-Paul Bezemer et.al. | 2504.16180 | | | Abstract (click to expand)Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 146 relevant publications from the DBLP Computer Science Bibliography up to the end of 2024, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 64 of the 146 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells. |
| 2025-04-23 | Fast Higher-Order Interpolation and Restriction in ExaHyPE Avoiding Non-physical Reflections | Timothy Stokes, Tobias Weinzierl, Han Zhang et.al. | 2504.15814 | | | Abstract (click to expand)Wave equations help us to understand phenomena ranging from earthquakes to tsunamis. These phenomena materialise over very large scales. It would be computationally infeasible to track them over a regular mesh. Yet, since the phenomena are localised, adaptive mesh refinement (AMR) can be used to construct meshes with a higher resolution close to the regions of interest. ExaHyPE is a software engine created to solve wave problems using AMR, and we use it as baseline to construct our numerical relativity application called ExaGRyPE. To advance the mesh in time, we have to interpolate and restrict along resolution transitions in each and every time step. ExaHyPE's vanilla code version uses a d-linear tensor-product approach. In benchmarks of a stationary black hole this performs slowly and leads to errors in conserved quantities near AMR boundaries. We therefore introduce a set of higher-order interpolation schemes where the derivatives are calculated at each coarse grid cell to approximate the enclosed fine cells. The resulting methods run faster than the tensor-product approach. Most importantly, when running the stationary black hole simulation using the higher order methods the errors near the AMR boundaries are removed. |
| 2025-04-21 | A dual-stage constitutive modeling framework based on finite strain data-driven identification and physics-augmented neural networks | Lennart Linden, Karl A. Kalina, Jörg Brummund et.al. | 2504.15492 | | | Abstract (click to expand)In this contribution, we present a novel consistent dual-stage approach for the automated generation of hyperelastic constitutive models which only requires experimentally measurable data. To generate input data for our approach, an experiment with full-field measurement has to be conducted to gather testing force and corresponding displacement field of the sample. Then, in the first step of the dual-stage framework, a new finite strain Data-Driven Identification (DDI) formulation is applied. This method enables to identify tuples consisting of stresses and strains by only prescribing the applied boundary conditions and the measured displacement field. In the second step, the data set is used to calibrate a Physics-Augmented Neural Network (PANN), which fulfills all common conditions of hyperelasticity by construction and is very flexible at the same time. We demonstrate the applicability of our approach by several descriptive examples. Two-dimensional synthetic data are exemplarily generated in virtual experiments by using a reference constitutive model. The calibrated PANN is then applied in 3D Finite Element simulations. In addition, a real experiment including noisy data is mimicked. |
| 2025-04-21 | Speaker Fuzzy Fingerprints: Benchmarking Text-Based Identification in Multiparty Dialogues | Rui Ribeiro, Luísa Coheur, Joao P. Carvalho et.al. | 2504.14963 | | Paper accepted at the FUZZY IEEE 2025 conference | Abstract (click to expand)Speaker identification using voice recordings leverages unique acoustic features, but this approach fails when only textual data is available. Few approaches have attempted to tackle the problem of identifying speakers solely from text, and the existing ones have primarily relied on traditional methods. In this work, we explore the use of fuzzy fingerprints from large pre-trained models to improve text-based speaker identification. We integrate speaker-specific tokens and context-aware modeling, demonstrating that conversational context significantly boosts accuracy, reaching 70.6% on the Friends dataset and 67.7% on the Big Bang Theory dataset. Additionally, we show that fuzzy fingerprints can approximate full fine-tuning performance with fewer hidden units, offering improved interpretability. Finally, we analyze ambiguous utterances and propose a mechanism to detect speaker-agnostic lines. Our findings highlight key challenges and provide insights for future improvements in text-based speaker identification. |
| 2025-04-21 | EducationQ: Evaluating LLMs' Teaching Capabilities Through Multi-Agent Dialogue Framework | Yao Shi, Rongkeng Liang, Yong Xu et.al. | 2504.14928 | | | Abstract (click to expand)Large language models (LLMs) increasingly serve as educational tools, yet evaluating their teaching capabilities remains challenging due to the resource-intensive, context-dependent, and methodologically complex nature of teacher-student interactions. We introduce EducationQ, a multi-agent dialogue framework that efficiently assesses teaching capabilities through simulated dynamic educational scenarios, featuring specialized agents for teaching, learning, and evaluation. Testing 14 LLMs across major AI Organizations (OpenAI, Meta, Google, Anthropic, and others) on 1,498 questions spanning 13 disciplines and 10 difficulty levels reveals that teaching effectiveness does not correlate linearly with model scale or general reasoning capabilities - with some smaller open-source models outperforming larger commercial counterparts in teaching contexts. This finding highlights a critical gap in current evaluations that prioritize knowledge recall over interactive pedagogy. Our mixed-methods evaluation, combining quantitative metrics with qualitative analysis and expert case studies, identifies distinct pedagogical strengths employed by top-performing models (e.g., sophisticated questioning strategies, adaptive feedback mechanisms). Human expert evaluations show 78% agreement with our automated qualitative analysis of effective teaching behaviors, validating our methodology. EducationQ demonstrates that LLMs-as-teachers require specialized optimization beyond simple scaling, suggesting next-generation educational AI prioritize targeted enhancement of specific pedagogical effectiveness. |
| 2025-04-23 | Physics-Aware Compression of Plasma Distribution Functions with GPU-Accelerated Gaussian Mixture Models | Andong Hu, Luca Pennati, Ivy Peng et.al. | 2504.14897 | | 15 pages, 8 figures | Abstract (click to expand)Data compression is a critical technology for large-scale plasma simulations. Storing complete particle information requires Terabyte-scale data storage, and analysis requires ad-hoc scalable post-processing tools. We propose a physics-aware in-situ compression method using Gaussian Mixture Models (GMMs) to approximate electron and ion velocity distribution functions with a number of Gaussian components. This GMM-based method allows us to capture plasma features such as mean velocity and temperature, and it enables us to identify heating processes and generate beams. We first construct a histogram to reduce computational overhead and apply GPU-accelerated, in-situ GMM fitting within iPIC3D, a large-scale implicit Particle-in-Cell simulator, ensuring real-time compression. The compressed representation is stored using the ADIOS 2 library, thus optimizing the I/O process. The GPU and histogramming implementation provides a significant speed-up with respect to GMM on particles (both in time and required memory at run-time), enabling real-time compression. Compared to algorithms like SZ, MGARD, and BLOSC2, our GMM-based method has a physics-based approach, retaining the physical interpretation of plasma phenomena such as beam formation, acceleration, and heating mechanisms. Our GMM algorithm achieves a compression ratio of up to \(10^4\) , requiring a processing time comparable to, or even lower than, standard compression engines. |
| 2025-04-19 | A parallel implementation of reduced-order modeling of large-scale systems | Ionut-Gabriel Farcas, Rayomand P. Gundevia, Ramakanth Munipalli et.al. | 2504.14338 | link | 19 pages, 4 figures; the corresponding code can be found at https://github.com/ionutfarcas/distributed_Operator_Inference | Abstract (click to expand)Motivated by the large-scale nature of modern aerospace engineering simulations, this paper presents a detailed description of distributed Operator Inference (dOpInf), a recently developed parallel algorithm designed to efficiently construct physics-based reduced-order models (ROMs) for problems with large state dimensions. One such example is the simulation of rotating detonation rocket engines, where snapshot data generated by high-fidelity large-eddy simulations have many millions of degrees of freedom. dOpInf enables, via distributed computing, the efficient processing of datasets with state dimensions that are too large to process on a single computer, and the learning of structured physics-based ROMs that approximate the dynamical systems underlying those datasets. All elements of dOpInf are scalable, leading to a fully parallelized reduced modeling approach that can scale to the thousands of processors available on leadership high-performance computing platforms. The resulting ROMs are computationally cheap, making them ideal for key engineering tasks such as design space exploration, risk assessment, and uncertainty quantification. To illustrate the practical application of dOpInf, we provide a step-by-step tutorial using a 2D Navier-Stokes flow over a step scenario as a case study. This tutorial guides users through the implementation process, making dOpInf accessible for integration into complex aerospace engineering simulations. |
| 2025-04-18 | Transformer Encoder and Multi-features Time2Vec for Financial Prediction | Nguyen Kim Hai Bui, Nguyen Duy Chien, Péter Kovács et.al. | 2504.13801 | | 5 pages, currently under review at Eusipco 2025 | Abstract (click to expand)Financial prediction is a complex and challenging task of time series analysis and signal processing, expected to model both short-term fluctuations and long-term temporal dependencies. Transformers have remarkable success mostly in natural language processing using attention mechanism, which also influenced the time series community. The ability to capture both short and long-range dependencies helps to understand the financial market and to recognize price patterns, leading to successful applications of Transformers in stock prediction. Although, the previous research predominantly focuses on individual features and singular predictions, that limits the model's ability to understand broader market trends. In reality, within sectors such as finance and technology, companies belonging to the same industry often exhibit correlated stock price movements. In this paper, we develop a novel neural network architecture by integrating Time2Vec with the Encoder of the Transformer model. Based on the study of different markets, we propose a novel correlation feature selection method. Through a comprehensive fine-tuning of multiple hyperparameters, we conduct a comparative analysis of our results against benchmark models. We conclude that our method outperforms other state-of-the-art encoding methods such as positional encoding, and we also conclude that selecting correlation features enhance the accuracy of predicting multiple stock prices. |
| 2025-04-22 | Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems | Vinay Sharma, Rémi Tanguy Oddon, Pietro Tesini et.al. | 2504.13768 | | permission not yet received for arXiv | Abstract (click to expand)Accurate real-time modeling of multi-body dynamical systems is essential for enabling digital twin applications across industries. While many data-driven approaches aim to learn system dynamics, jointly predicting internal loads and system trajectories remains a key challenge. This dual prediction is especially important for fault detection and predictive maintenance, where internal loads-such as contact forces-act as early indicators of faults, reflecting wear or misalignment before affecting motion. These forces also serve as inputs to degradation models (e.g., crack growth), enabling damage prediction and remaining useful life estimation. We propose Equi-Euler GraphNet, a physics-informed graph neural network (GNN) that simultaneously predicts internal forces and global trajectories in multi-body systems. In this mesh-free framework, nodes represent system components and edges encode interactions. Equi-Euler GraphNet introduces two inductive biases: (1) an equivariant message-passing scheme, interpreting edge messages as interaction forces consistent under Euclidean transformations; and (2) a temporal-aware iterative node update mechanism, based on Euler integration, to capture influence of distant interactions over time. Tailored for cylindrical roller bearings, it decouples ring dynamics from constrained motion of rolling elements. Trained on high-fidelity multiphysics simulations, Equi-Euler GraphNet generalizes beyond the training distribution, accurately predicting loads and trajectories under unseen speeds, loads, and configurations. It outperforms state-of-the-art GNNs focused on trajectory prediction, delivering stable rollouts over thousands of time steps with minimal error accumulation. Achieving up to a 200x speedup over conventional solvers while maintaining comparable accuracy, it serves as an efficient reduced-order model for digital twins, design, and maintenance. |
| 2025-04-18 | Bitcoin's Edge: Embedded Sentiment in Blockchain Transactional Data | Charalampos Kleitsikas, Nikolaos Korfiatis, Stefanos Leonardos et.al. | 2504.13598 | | Published in IEEE International Conference on Blockchain and Cryptocurrency 2025 | Abstract (click to expand)Cryptocurrency blockchains, beyond their primary role as distributed payment systems, are increasingly used to store and share arbitrary content, such as text messages and files. Although often non-financial, this hidden content can impact price movements by conveying private information, shaping sentiment, and influencing public opinion. However, current analyses of such data are limited in scope and scalability, primarily relying on manual classification or hand-crafted heuristics. In this work, we address these limitations by employing Natural Language Processing techniques to analyze, detect patterns, and extract public sentiment encoded within blockchain transactional data. Using a variety of Machine Learning techniques, we showcase for the first time the predictive power of blockchain-embedded sentiment in forecasting cryptocurrency price movements on the Bitcoin and Ethereum blockchains. Our findings shed light on a previously underexplored source of freely available, transparent, and immutable data and introduce blockchain sentiment analysis as a novel and robust framework for enhancing financial predictions in cryptocurrency markets. Incidentally, we discover an asymmetry between cryptocurrencies; Bitcoin has an informational advantage over Ethereum in that the sentiment embedded into transactional data is sufficient to predict its price movement. |
| 2025-04-21 | Deep Learning Models Meet Financial Data Modalities | Kasymkhan Khubiev, Mikhail Semenov et.al. | 2504.13521 | | 15 pages, 14 images, 7 tables | Abstract (click to expand)Algorithmic trading relies on extracting meaningful signals from diverse financial data sources, including candlestick charts, order statistics on put and canceled orders, traded volume data, limit order books, and news flow. While deep learning has demonstrated remarkable success in processing unstructured data and has significantly advanced natural language processing, its application to structured financial data remains an ongoing challenge. This study investigates the integration of deep learning models with financial data modalities, aiming to enhance predictive performance in trading strategies and portfolio optimization. We present a novel approach to incorporating limit order book analysis into algorithmic trading by developing embedding techniques and treating sequential limit order book snapshots as distinct input channels in an image-based representation. Our methodology for processing limit order book data achieves state-of-the-art performance in high-frequency trading algorithms, underscoring the effectiveness of deep learning in financial applications. |
| 2025-04-18 | Ascribe New Dimensions to Scientific Data Visualization with VR | Daniela Ushizima, Guilherme Melo dos Santos, Zineb Sordo et.al. | 2504.13448 | | | Abstract (click to expand)For over half a century, the computer mouse has been the primary tool for interacting with digital data, yet it remains a limiting factor in exploring complex, multi-scale scientific images. Traditional 2D visualization methods hinder intuitive analysis of inherently 3D structures. Virtual Reality (VR) offers a transformative alternative, providing immersive, interactive environments that enhance data comprehension. This article introduces ASCRIBE-VR, a VR platform of Autonomous Solutions for Computational Research with Immersive Browsing \& Exploration, which integrates AI-driven algorithms with scientific images. ASCRIBE-VR enables multimodal analysis, structural assessments, and immersive visualization, supporting scientific visualization of advanced datasets such as X-ray CT, Magnetic Resonance, and synthetic 3D imaging. Our VR tools, compatible with Meta Quest, can consume the output of our AI-based segmentation and iterative feedback processes to enable seamless exploration of large-scale 3D images. By merging AI-generated results with VR visualization, ASCRIBE-VR enhances scientific discovery, bridging the gap between computational analysis and human intuition in materials research, connecting human-in-the-loop with digital twins. |
| 2025-04-17 | CardioFit: A WebGL-Based Tool for Fast and Efficient Parameterization of Cardiac Action Potential Models to Fit User-Provided Data | Darby I. Cairns, Maxfield R. Comstock, Flavio H. Fenton et.al. | 2504.13274 | | Darby I. Cairns and Maxfield R. Comstock contributed equally to this work | Abstract (click to expand)Cardiac action potential models allow examination of a variety of cardiac dynamics, including how behavior may change under specific interventions. To study a specific scenario, including patient-specific cases, model parameter sets must be found that accurately reproduce the dynamics of interest. To facilitate this complex and time-consuming process, we present an interactive browser-based tool that uses the particle swarm optimization (PSO) algorithm implemented in JavaScript and taking advantage of the WebGL API for hardware acceleration. Our tool allows rapid customization and can find low-error fittings to user-provided voltage time series or action potential duration data from multiple cycle lengths in a few iterations (10-32), corresponding to a runtime of a few seconds on most machines. Additionally, our tool focuses on ease of use and flexibility, providing a webpage interface that allows users to select a subset of parameters to fit, set the range of values each parameter is allowed to assume, and control the PSO algorithm hyperparameters. We demonstrate our tool's utility by fitting a variety of models to different datasets, showing how convergence is affected by model choice, dataset properties, and PSO algorithmic settings, and explaining new insights gained about the physiological and dynamical roles of the model parameters. |
| 2025-04-17 | Fast and Accurate Prediction of Antenna Reflection Coefficients in Planar Layered Media Environment via Generalized Scattering Matrix | Chenbo Shi, Shichen Liang, Xin Gu et.al. | 2504.12613 | | | Abstract (click to expand)The numerical algorithm for evaluating the reflection coefficient of an antenna in the presence of the planar layered medium is reformulated using the antenna's generalized scattering matrix (GSM). The interaction between the antenna and the layered medium is modeled through spherical-to-planar vector wave transformations, ensuring no approximations that could compromise computational accuracy. This theoretical framework significantly reduces algebraic complexity, resulting in a marked increase in the speed of antenna performance evaluation. Excluding the one-time preprocessing cost of obtaining the antenna's GSM in free space, the numerical evaluation speed of this method exceeds that of the commercial software FEKO by several orders of magnitude, while maintaining nearly identical accuracy. |
| 2025-04-16 | Continual Learning Strategies for 3D Engineering Regression Problems: A Benchmarking Study | Kaira M. Samuel, Faez Ahmed et.al. | 2504.12503 | | | Abstract (click to expand)Engineering problems that apply machine learning often involve computationally intensive methods but rely on limited datasets. As engineering data evolves with new designs and constraints, models must incorporate new knowledge over time. However, high computational costs make retraining models from scratch infeasible. Continual learning (CL) offers a promising solution by enabling models to learn from sequential data while mitigating catastrophic forgetting, where a model forgets previously learned mappings. This work introduces CL to engineering design by benchmarking several CL methods on representative regression tasks. We apply these strategies to five engineering datasets and construct nine new engineering CL benchmarks to evaluate their ability to address forgetting and improve generalization. Preliminary results show that applying existing CL methods to these tasks improves performance over naive baselines. In particular, the Replay strategy achieved performance comparable to retraining in several benchmarks while reducing training time by nearly half, demonstrating its potential for real-world engineering workflows. The code and datasets used in this work will be available at: https://github.com/kmsamuel/cl-for-engineering-release. |
| 2025-04-16 | Deep Material Network: Overview, applications and current directions | Ting-Ju Wei, Wen-Ning Wan, Chuin-Shan Chen et.al. | 2504.12159 | | | Abstract (click to expand)Deep Material Network (DMN) has emerged as a powerful framework for multiscale material modeling, enabling efficient and accurate predictions of material behavior across different length scales. Unlike traditional machine learning approaches, the trainable parameters in DMN have direct physical interpretations, capturing the geometric characteristics of the microstructure rather than serving as purely statistical fitting parameters. Its hierarchical tree structure effectively encodes microstructural interactions and deformation mechanisms, allowing DMN to achieve a balance between accuracy and computational efficiency. This physics-informed architecture significantly reduces computational costs compared to direct numerical simulations while preserving essential microstructural physics. Furthermore, DMN can be trained solely on a linear elastic dataset while effectively extrapolating nonlinear responses during online prediction, making it a highly efficient and scalable approach for multiscale material modeling. This article provides a comprehensive review of DMN, detailing its motivation, underlying methodology, and recent advancements. We discuss key modeling aspects, including its hierarchical structure, training process, and the role of physics-based constraints in enhancing predictive accuracy. Furthermore, we highlight its applications in component-scale multiscale analysis and inverse parameter identification, demonstrating its capability to bridge microscale material behavior with macroscale engineering predictions. Finally, we discuss challenges and future directions in improving DMN's generalization capabilities and its potential extensions for broader applications in multiscale modeling. |
| 2025-04-16 | A viscoplasticity model with an invariant-based non-Newtonian flow rule for unidirectional thermoplastic composites | P. Hofman, D. Kovačević, F. P. van der Meer et.al. | 2504.12069 | | 40 pages, 20 figures, 3 tables | Abstract (click to expand)A three-dimensional mesoscopic viscoplasticity model for simulating rate-dependent plasticity and creep in unidirectional thermoplastic composites is presented. The constitutive model is a transversely isotropic extension of an isotropic finite strain viscoplasticity model for neat polymers. Rate-dependent plasticity and creep are described by a non-Newtonian flow rule where the viscosity of the material depends on an equivalent stress measure through an Eyring-type relation. In the present formulation, transverse isotropy is incorporated by defining the equivalent stress measure and flow rule as functions of transversely isotropic stress invariants. In addition, the Eyring-type viscosity function is extended with anisotropic pressure dependence. As a result of the formulation, plastic flow in fiber direction is effectively excluded and pressure dependence of the polymer matrix is accounted for. The re-orientation of the transversely isotropic plane during plastic deformations is incorporated in the constitutive equations, allowing for an accurate large deformation response. The formulation is fully implicit and a consistent linearization of the algorithmic constitutive equations is performed to derive the consistent tangent modulus. The performance of the mesoscopic constitutive model is assessed through a comparison with a micromechanical model for carbon/PEEK, with the original isotropic viscoplastic version for the polymer matrix and with hyperelastic fibers. The micromodel is first used to determine the material parameters of the mesoscale model with a few stress-strain curves. It is demonstrated that the mesoscale model gives a similar response to the micromodel under various loading conditions. Finally, the mesoscale model is validated against off-axis experiments on unidirectional thermoplastic composite plies. |
| 2025-04-16 | Topological Analysis of Mixer Activities in the Bitcoin Network | Francesco Zola, Jon Ander Medina, Andrea Venturi et.al. | 2504.11924 | | The paper is presented at the 3rd IEEE International Workshop on Cryptocurrency Exchanges (CryptoEx 2025) | Abstract (click to expand)Cryptocurrency users increasingly rely on obfuscation techniques such as mixers, swappers, and decentralised or no-KYC exchanges to protect their anonymity. However, at the same time, these services are exploited by criminals to conceal and launder illicit funds. Among obfuscation services, mixers remain one of the most challenging entities to tackle. This is because their owners are often unwilling to cooperate with Law Enforcement Agencies, and technically, they operate as 'black boxes'. To better understand their functionalities, this paper proposes an approach to analyse the operations of mixers by examining their address-transaction graphs and identifying topological similarities to uncover common patterns that can define the mixer's modus operandi. The approach utilises community detection algorithms to extract dense topological structures and clustering algorithms to group similar communities. The analysis is further enriched by incorporating data from external sources related to known Exchanges, in order to understand their role in mixer operations. The approach is applied to dissect the Blender.io mixer activities within the Bitcoin blockchain, revealing: i) consistent structural patterns across address-transaction graphs; ii) that Exchanges play a key role, following a well-established pattern, which raises several concerns about their AML/KYC policies. This paper represents an initial step toward dissecting and understanding the complex nature of mixer operations in cryptocurrency networks and extracting their modus operandi. |
| 2025-04-15 | Magnetic Field Conforming Formulations for Foil Windings | Louis Denis, Elias Paakkunainen, Paavo Rasilo et.al. | 2504.11191 | | This work has been submitted to the IEEE for possible publication | Abstract (click to expand)We extend the foil winding homogenization method to magnetic field conforming formulations. We first propose a full magnetic field foil winding formulation by analogy with magnetic flux density conforming formulations. We then introduce the magnetic scalar potential in non-conducting regions to improve the efficiency of the model. This leads to a significant reduction in the number of degrees of freedom, particularly in 3-D applications. The proposed models are verified on two frequency-domain benchmark problems: a 2-D axisymmetric problem and a 3-D problem. They reproduce results obtained with magnetic flux density conforming formulations and with resolved conductor models that explicitly discretize all turns. Moreover, the models are applied in the transient simulation of a high-temperature superconducting coil. In all investigated configurations, the proposed models provide reliable results while considerably reducing the size of the numerical problem to be solved. |
| 2025-04-15 | A study of troubled-cell indicators applied to finite volume methods using a novel monotonicity parameter | R. Shivananda Rao, M. Ramakrishna et.al. | 2504.11056 | | | Abstract (click to expand)We adapt a troubled-cell indicator developed for discontinuous Galerkin (DG) methods to the finite volume method (FVM) framework for solving hyperbolic conservation laws. This indicator depends solely on the cell-average data of the target cell and its immediate neighbours. Once the troubled-cells are identified, we apply the limiter only in these cells instead of applying in all computational cells. We introduce a novel technique to quantify the quality of the solution in the neighbourhood of the shock by defining a monotonicity parameter \(\mu\) . Numerical results from various two-dimensional simulations on the hyperbolic systems of Euler equations using a finite volume solver employing MUSCL reconstruction validate the performance of the troubled-cell indicator and the approach of limiting only in the troubled-cells. These results show that limiting only in the troubled-cells is preferable to limiting everywhere as it improves convergence without compromising on the solution accuracy. |
| 2025-04-15 | Design and Verification of a Synchronus First In First Out (FIFO) | Yatheeswar Penta, Riadul Islam et.al. | 2504.10901 | | | Abstract (click to expand)This project focuses on designing and verifying a synchronous FIFO First In First Out (FIFO) memory, a critical component in digital systems for temporary data storage and seamless data transfer. The FIFO operates under a single clock domain, ensuring synchronized read and write operations, making it suitable for systems requiring high-speed, reliable data buffering. This design includes FIFO's key features, such as read and write operations, full and empty flag generation, and pointer management for memory control. The FIFO was implemented using Verilog to define the Register Transfer Level (RTL) design, ensuring functionality and timing requirements were met. For verification, three approaches were employed: (1) UVM-based Verification: A Universal Verification Methodology (UVM) testbench was developed to test the FIFO design rigorously. The testbench includes components like interface, sequence item, driver, monitor, scoreboard, agent, and environment. Directed and random tests were performed to verify corner cases, such as simultaneous reads and writes, full and empty conditions, and overflow and underflow scenarios; (2) Traditional Verilog Testbench: A standalone Verilog testbench was also used to validate the functionality of the FIFO through directed test scenarios and waveform analysis; (3) FPGA implementation: Additionally, the design was implemented on an FPGA for real-time testing to verify its functionality and timing behavior in hardware. FPGA-based verification ensured the design performed as expected under practical conditions. The results confirmed the correct operation of the FIFO, including accurate data transfer, flag behavior, and timing synchronization. The project successfully demonstrated the robustness and reliability of the synchronous FIFO design, highlighting its importance in modern digital systems for efficient data handling and buffering. |
| 2025-04-14 | Finding Pathways in Reaction Networks guided by Energy Barriers using Integer Linear Programming | Adittya Pal, Rolf Fagerberg, Jakob Lykke Andersen et.al. | 2504.10609 | | | Abstract (click to expand)Analyzing synthesis pathways for target molecules in a chemical reaction network annotated with information on the kinetics of individual reactions is an area of active study. This work presents a computational methodology for searching for pathways in reaction networks which is based on integer linear programming and the modeling of reaction networks by directed hypergraphs. Often multiple pathways fit the given search criteria. To rank them, we develop an objective function based on physical arguments maximizing the probability of the pathway. We furthermore develop an automated pipeline to estimate the energy barriers of individual reactions in reaction networks. Combined, the methodology facilitates flexible and kinetically informed pathway investigations on large reaction networks by computational means, even for networks coming without kinetic annotation, such as those created via generative approaches for expanding molecular spaces. |
| 2025-04-09 | Artificial Intelligence and the Dual Paradoxes: Examining the Interplay of Efficiency, Resource Consumption, and Labor Dynamics | Mfon Akpan, Adeyemi Adebayo et.al. | 2504.10503 | | 27 pages | Abstract (click to expand)Artificial Intelligence's (AI) rapid development and growth not only transformed industries but also fired up important debates about its impacts on employment, resource allocation, and the ethics involved in decision-making. It serves to understand how changes within an industry will be able to influence society with that change. Advancing AI technologies will create a dual paradox of efficiency, greater resource consumption, and displacement of traditional labor. In this context, we explore the impact of AI on energy consumption, human labor roles, and hybrid roles widespread human labor replacement. We used mixed methods involving qualitative and quantitative analyses of data identified from various sources. Findings suggest that AI increases energy consumption and has impacted human labor roles to a minimal extent, considering that its applicability is limited to some tasks that require human judgment. In this context, the |
| 2025-04-14 | Unleashing Expert Opinion from Social Media for Stock Prediction | Wanyun Zhou, Saizhuo Wang, Xiang Li et.al. | 2504.10078 | link | | Abstract (click to expand)While stock prediction task traditionally relies on volume-price and fundamental data to predict the return ratio or price movement trend, sentiment factors derived from social media platforms such as StockTwits offer a complementary and useful source of real-time market information. However, we find that most social media posts, along with the public sentiment they reflect, provide limited value for trading predictions due to their noisy nature. To tackle this, we propose a novel dynamic expert tracing algorithm that filters out non-informative posts and identifies both true and inverse experts whose consistent predictions can serve as valuable trading signals. Our approach achieves significant improvements over existing expert identification methods in stock trend prediction. However, when using binary expert predictions to predict the return ratio, similar to all other expert identification methods, our approach faces a common challenge of signal sparsity with expert signals cover only about 4% of all stock-day combinations in our dataset. To address this challenge, we propose a dual graph attention neural network that effectively propagates expert signals across related stocks, enabling accurate prediction of return ratios and significantly increasing signal coverage. Empirical results show that our propagated expert-based signals not only exhibit strong predictive power independently but also work synergistically with traditional financial features. These combined signals significantly outperform representative baseline models in all quant-related metrics including predictive accuracy, return metrics, and correlation metrics, resulting in more robust investment strategies. We hope this work inspires further research into leveraging social media data for enhancing quantitative investment strategies. The code can be seen in https://github.com/wanyunzh/DualGAT. |
| 2025-04-14 | BO-SA-PINNs: Self-adaptive physics-informed neural networks based on Bayesian optimization for automatically designing PDE solvers | Rui Zhang, Liang Li, Stéphane Lanteri et.al. | 2504.09804 | link | 23 pages, 5 figure | Abstract (click to expand)Physics-informed neural networks (PINNs) is becoming a popular alternative method for solving partial differential equations (PDEs). However, they require dedicated manual modifications to the hyperparameters of the network, the sampling methods and loss function weights for different PDEs, which reduces the efficiency of the solvers. In this paper, we pro- pose a general multi-stage framework, i.e. BO-SA-PINNs to alleviate this issue. In the first stage, Bayesian optimization (BO) is used to select hyperparameters for the training process, and based on the results of the pre-training, the network architecture, learning rate, sampling points distribution and loss function weights suitable for the PDEs are automatically determined. The proposed hyperparameters search space based on experimental results can enhance the efficiency of BO in identifying optimal hyperparameters. After selecting the appropriate hyperparameters, we incorporate a global self-adaptive (SA) mechanism the second stage. Using the pre-trained model and loss information in the second-stage training, the exponential moving average (EMA) method is employed to optimize the loss function weights, and residual-based adaptive refinement with distribution (RAR-D) is used to optimize the sampling points distribution. In the third stage, L-BFGS is used for stable training. In addition, we introduce a new activation function that enables BO-SA-PINNs to achieve higher accuracy. In numerical experiments, we conduct comparative and ablation experiments to verify the performance of the model on Helmholtz, Maxwell, Burgers and high-dimensional Poisson equations. The comparative experiment results show that our model can achieve higher accuracy and fewer iterations in test cases, and the ablation experiments demonstrate the positive impact of every improvement. |
| 2025-04-13 | Earthquake Simulation | Palak Chawla et.al. | 2504.09673 | | 4 pages | Abstract (click to expand)This paper presents a seismic activity simulator that models the effects of fault lines on surface pressure. This project uses C programming to create a fully interactive learning resource intended to educate users on the mechanics of earthquakes. The motivation behind this project is to make studying seismic activity more accessible, engaging and cost effective. |
| 2025-04-16 | Causal integration of chemical structures improves representations of microscopy images for morphological profiling | Yemin Yu, Neil Tenenholtz, Lester Mackey et.al. | 2504.09544 | link | 24 pages | Abstract (click to expand)Recent advances in self-supervised deep learning have improved our ability to quantify cellular morphological changes in high-throughput microscopy screens, a process known as morphological profiling. However, most current methods only learn from images, despite many screens being inherently multimodal, as they involve both a chemical or genetic perturbation as well as an image-based readout. We hypothesized that incorporating chemical compound structure during self-supervised pre-training could improve learned representations of images in high-throughput microscopy screens. We introduce a representation learning framework, MICON (Molecular-Image Contrastive Learning), that models chemical compounds as treatments that induce counterfactual transformations of cell phenotypes. MICON significantly outperforms classical hand-crafted features such as CellProfiler and existing deep-learning-based representation learning methods in challenging evaluation settings where models must identify reproducible effects of drugs across independent replicates and data-generating centers. We demonstrate that incorporating chemical compound information into the learning process provides consistent improvements in our evaluation setting and that modeling compounds specifically as treatments in a causal framework outperforms approaches that directly align images and compounds in a single representation space. Our findings point to a new direction for representation learning in morphological profiling, suggesting that methods should explicitly account for the multimodal nature of microscopy screening data. |
| 2025-04-11 | Exponential Shift: Humans Adapt to AI Economies | Kevin J McNamara, Rhea Pritham Marpu et.al. | 2504.08855 | | | Abstract (click to expand)This paper explores how artificial intelligence (AI) and robotics are transforming the global labor market. Human workers, limited to a 33% duty cycle due to rest and holidays, cost $14 to $55 per hour. In contrast, digital labor operates nearly 24/7 at just \(0.10 to\) 0.50 per hour. We examine sectors like healthcare, education, manufacturing, and retail, finding that 40-70% of tasks could be automated. Yet, human skills like emotional intelligence and adaptability remain essential. Humans process 5,000-20,000 tokens (units of information) per hour, while AI far exceeds this, though its energy use-3.5 to 7 times higher than humans-could offset 20-40% of cost savings. Using real-world examples, such as AI in journalism and law, we illustrate these dynamics and propose six strategies-like a 4-day workweek and retraining-to ensure a fair transition to an AI-driven economy. |
| 2025-04-11 | Exploring Cognitive Attributes in Financial Decision-Making | Mallika Mainali, Rosina O. Weber et.al. | 2504.08849 | | 7 pages, 2 figures. Presented in SIAM International Conference on Data Mining (SDM25) METACOG-25: 2nd Workshop on Metacognitive Prediction of AI Behavior | Abstract (click to expand)Cognitive attributes are fundamental to metacognition, shaping how individuals process information, evaluate choices, and make decisions. To develop metacognitive artificial intelligence (AI) models that reflect human reasoning, it is essential to account for the attributes that influence reasoning patterns and decision-maker behavior, often leading to different or even conflicting choices. This makes it crucial to incorporate cognitive attributes in designing AI models that align with human decision-making processes, especially in high-stakes domains such as finance, where decisions have significant real-world consequences. However, existing AI alignment research has primarily focused on value alignment, often overlooking the role of individual cognitive attributes that distinguish decision-makers. To address this issue, this paper (1) analyzes the literature on cognitive attributes, (2) establishes five criteria for defining them, and (3) categorizes 19 domain-specific cognitive attributes relevant to financial decision-making. These three components provide a strong basis for developing AI systems that accurately reflect and align with human decision-making processes in financial contexts. |
| 2025-04-09 | Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization | Zirui Chen, Zhaoyang Zhang, Ziqing Xing et.al. | 2504.08811 | link | | Abstract (click to expand)Existing learning models often exhibit poor generalization when deployed across diverse scenarios. It is mainly due to that the underlying reference frame of the data varies with the deployment environment and settings. However, despite the data of each scenario has its distinct reference frame, its generation generally follows the same underlying physical rule. Based on these findings, this article proposes a brand-new universal deep learning framework named analogical learning (AL), which provides a highly efficient way to implicitly retrieve the reference frame information associated with a scenario and then to make accurate prediction by relative analogy across scenarios. Specifically, an elegant bipartite neural network architecture called Mateformer is designed, the first part of which calculates the relativity within multiple feature spaces between the input data and a small amount of embedded data from the current scenario, while the second part uses these relativity to guide the nonlinear analogy. We apply AL to the typical multi-scenario learning problem of intelligent wireless localization in cellular networks. Extensive experiments show that AL achieves state-of-the-art accuracy, stable transferability and robust adaptation to new scenarios without any tuning, and outperforming conventional methods with a precision improvement of nearly two orders of magnitude. All data and code are available at https://github.com/ziruichen-research/ALLoc. |
| 2025-04-11 | Control Co-Design Under Uncertainty for Offshore Wind Farms: Optimizing Grid Integration, Energy Storage, and Market Participation | Himanshu Sharma, Wei Wang, Bowen Huang et.al. | 2504.08555 | | | Abstract (click to expand)Offshore wind farms (OWFs) are set to significantly contribute to global decarbonization efforts. Developers often use a sequential approach to optimize design variables and market participation for grid-integrated offshore wind farms. However, this method can lead to sub-optimal system performance, and uncertainties associated with renewable resources are often overlooked in decision-making. This paper proposes a control co-design approach, optimizing design and control decisions for integrating OWFs into the power grid while considering energy market and primary frequency market participation. Additionally, we introduce optimal sizing solutions for energy storage systems deployed onshore to enhance revenue for OWF developers over time. This framework addresses uncertainties related to wind resources and energy prices. We analyze five U.S. west-coast offshore wind farm locations and potential interconnection points, as identified by the Bureau of Ocean Energy Management (BOEM). Results show that optimized control co-design solutions can increase market revenue by 3.2\% and provide flexibility in managing wind resource uncertainties. |
| 2025-04-11 | Approximation Algorithms for the UAV Path Planning with Object Coverage Constraints | Jiawei Wang, Vincent Chau, Weiwei Wu et.al. | 2504.08405 | | | Abstract (click to expand)We study the problem of the Unmanned Aerial Vehicle (UAV) such that a specific set of objects needs to be observed while ensuring a quality of observation. Our goal is to determine the shortest path for the UAV. This paper proposes an offline algorithm with an approximation of \((2+2n)(1+\epsilon)\) where \(\epsilon >0\) is a small constant, and \(n\) is the number of objects. We then propose several online algorithms in which objects are discovered during the process. To evaluate the performance of these algorithms, we conduct experimental comparisons. Our results show that the online algorithms perform similarly to the offline algorithm, but with significantly faster execution times ranging from 0.01 seconds to 200 seconds. We also show that our methods yield solutions with costs comparable to those obtained by the Gurobi optimizer that requires 30000 seconds of runtime. |
| 2025-04-11 | A 120 lines code for isogeometric topology optimization and its extension to 3D in MATLAB | Xianda Xie, Zhihui Ou, Aodi Yang et.al. | 2504.08233 | | | Abstract (click to expand)In this paper, a compact and efficient code implementation is presented for isogeometric topology optimization (ITO) approach. With the aid of B.ezier extraction technique, a derived explicit stiffness matrix computation formula is applied to all B-spline IGA elements with rectangular shape under linear elasticity assumption. Using the aforementioned explicit formula, the stiffness matrix calculation and updating of IGA are significantly simplified, which leads to the current ITO code implemented only in one main function without calling subroutines, such as IGA mesh generation and Gaussian quadrature. Both two-dimensional (2D) and three-dimensional (3D) cases are taken into consideration, which result into iga_top120 and iga_top3D257 MATLAB codes for 2D and 3D design problems. Numerical examples validate the effectiveness of our open-source codes, with several user-defined input parameters basically identical to those used in top88 and top3D. Therefore, iga_top120 and iga_top3D257 provide an effective entry for the code transforming from FEM-based TO into ITO. |
| 2025-04-17 | Variational quantum and neural quantum states algorithms for the linear complementarity problem | Saibal De, Oliver Knitter, Rohan Kodati et.al. | 2504.08141 | | 13 pages, 5 figures, to appear in Philosophical Transactions of the Royal Society A | Abstract (click to expand)Variational quantum algorithms (VQAs) are promising hybrid quantum-classical methods designed to leverage the computational advantages of quantum computing while mitigating the limitations of current noisy intermediate-scale quantum (NISQ) hardware. Although VQAs have been demonstrated as proofs of concept, their practical utility in solving real-world problems -- and whether quantum-inspired classical algorithms can match their performance -- remains an open question. We present a novel application of the variational quantum linear solver (VQLS) and its classical neural quantum states-based counterpart, the variational neural linear solver (VNLS), as key components within a minimum map Newton solver for a complementarity-based rigid body contact model. We demonstrate using the VNLS that our solver accurately simulates the dynamics of rigid spherical bodies during collision events. These results suggest that quantum and quantum-inspired linear algebra algorithms can serve as viable alternatives to standard linear algebra solvers for modeling certain physical systems. |
| 2025-04-10 | Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis | Fei-Hsuan Yu, Yun-Cheng Chou, Teng-Ruei Chen et.al. | 2504.07872 | | | Abstract (click to expand)We propose the Dual Engines of Thoughts (DEoT), an analytical framework for comprehensive open-ended reasoning. While traditional reasoning frameworks primarily focus on finding "the best answer" or "the correct answer" for single-answer problems, DEoT is specifically designed for "open-ended questions," enabling both broader and deeper analytical exploration. The framework centers on three key components: a Base Prompter for refining user queries, a Solver Agent that orchestrates task decomposition, execution, and validation, and a Dual-Engine System consisting of a Breadth Engine (to explore diverse impact factors) and a Depth Engine (to perform deep investigations). This integrated design allows DEoT to balance wide-ranging coverage with in-depth analysis, and it is highly customizable, enabling users to adjust analytical parameters and tool configurations based on specific requirements. Experimental results show that DEoT excels in addressing complex, multi-faceted questions, achieving a total win rate of 77-86% compared to existing reasoning models, thus highlighting its effectiveness in real-world applications. |
| 2025-04-10 | A Review of HPC-Accelerated CFD in National Security and Defense | James Afful et.al. | 2504.07837 | | | Abstract (click to expand)Using High-Performance Computing (HPC), Computational Fluid Dynamics (CFD) now serves as an essential component in defense-related national security applications including missile interception and hypersonic propulsion as well as naval stealth optimization and urban hazard dispersion. This review combines two decades of open-source and public-domain research on HPC-accelerated CFD in defense, addressing three key questions: Which security-sensitive simulations have utilized open-source CFD frameworks such as OpenFOAM, SU2 and ADflow? Which HPC techniques, such as MPI domain decomposition and GPU acceleration together with hybrid parallelism best enhance open-source frameworks to manage large defense CFD simulations? Which technological advancements and research voids currently drive the directional development of the field? Examining several research studies sourced from NASA, DoD HPC centers, and academic institutions, scientific contributions have been classified into air, maritime, and space domains. Modular frameworks like NavyFOAM and SU2 and ADflow's adjoint-based solvers show how custom open-source solutions support workflows with rapid completion of multi-million cell simulations. The conclusion highlights new trends that combine exascale readiness with machine learning surrogate models for real-time CFD applications and interdisciplinary HPC-driven multi-physics integration to deliver practical insights for improving CFD use in defense research and development. |
| 2025-04-10 | Benchmarking Image Embeddings for E-Commerce: Evaluating Off-the Shelf Foundation Models, Fine-Tuning Strategies and Practical Trade-offs | Urszula Czerwinska, Cenk Bircanoglu, Jeremy Chamoux et.al. | 2504.07567 | | accepted at Future Technologies Conference (FTC 2025) | Abstract (click to expand)We benchmark foundation models image embeddings for classification and retrieval in e-Commerce, evaluating their suitability for real-world applications. Our study spans embeddings from pre-trained convolutional and transformer models trained via supervised, self-supervised, and text-image contrastive learning. We assess full fine-tuning and transfer learning (top-tuning) on six diverse e-Commerce datasets: fashion, consumer goods, cars, food, and retail. Results show full fine-tuning consistently performs well, while text-image and self-supervised embeddings can match its performance with less training. While supervised embeddings remain stable across architectures, SSL and contrastive embeddings vary significantly, often benefiting from top-tuning. Top-tuning emerges as an efficient alternative to full fine-tuning, reducing computational costs. We also explore cross-tuning, noting its impact depends on dataset characteristics. Our findings offer practical guidelines for embedding selection and fine-tuning strategies, balancing efficiency and performance. |
| 2025-04-06 | SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized Finance | Abdulrahman Alhaidari, Bhavani Kalal, Balaji Palanisamy et.al. | 2504.07132 | link | Accepted paper to appear in the 15th ACM Conference on Data and Application Security and Privacy (CODASPY 2025) | Abstract (click to expand)Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users' trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana's transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation. |
| 2025-04-09 | Machine Learning (ML) based Reduced Order Modeling (ROM) for linear and non-linear solid and structural mechanics | Mikhael Tannous, Chady Ghnatios, Eivind Fonn et.al. | 2504.06860 | | | Abstract (click to expand)Multiple model reduction techniques have been proposed to tackle linear and non linear problems. Intrusive model order reduction techniques exhibit high accuracy levels, however, they are rarely used as a standalone industrial tool, because of the required high level knowledge involved in the construction and usage of these techniques. Moreover, the computation time benefit is compromised for highly nonlinear problems. On the other hand, non-intrusive methods often struggle with accuracy in nonlinear cases, typically requiring a large design of experiment and a large number of snapshots achieve a reliable performance. However, generating the stiffness matrix in a non-intrusive approach presents an optimal way to align accuracy with efficiency, allying the advantages of both intrusive and non-intrusive methods.This work introduces a lightly intrusive model order reduction technique that employs machine learning within a Proper Orthogonal Decomposition framework to achieve this alliance. By leveraging outputs from commercial full-order models, this method constructs a reduced-order model that operates effectively without requiring expert user intervention. The proposed technique has the possibility to approximate linear non affine as well as non linear terms. It is showcased for linear and nonlinear structural mechanics problems. |
| 2025-04-08 | Neural Network Enhanced Polyconvexification of Isotropic Energy Densities in Computational Mechanics | Loïc Balazi, Timo Neumeier, Malte A. Peter et.al. | 2504.06425 | | 24 pages, 14 figures | Abstract (click to expand)We present a neural network approach for fast evaluation of parameter-dependent polyconvex envelopes, which are crucial in computational mechanics. Our method uses a neural network architecture that inherently encodes polyconvexity in the main variable by combining a feature extraction layer that computes the minors function on the signed singular value characterisation of isotropic energy densities with a partially input convex neural network (PICNN). Polyconvex underestimation is weakly enforced by penalisation during training, as are the symmetries of the function. As a guiding example, we focus on a well-known isotropic damage problem, reformulated in terms of signed singular values, and apply a splitting approach to reduce the dimensionality of the parameter space, thereby making training more tractable. Numerical experiments show that the networks achieve sufficient accuracy for engineering applications while providing high compression and significant speed-up over traditional polyconvexification schemes. Most importantly, the network adapts to varying physical or material parameters, enabling real-time polyconvexification in large-scale computational mechanics scenarios. |
| 2025-04-08 | Quantum Annealing for Combinatorial Optimization: A Benchmarking Study | Seongmin Kim, Sang-Woo Ahn, In-Saeng Suh et.al. | 2504.06201 | | | Abstract (click to expand)Quantum annealing (QA) has the potential to significantly improve solution quality and reduce time complexity in solving combinatorial optimization problems compared to classical optimization methods. However, due to the limited number of qubits and their connectivity, the QA hardware did not show such an advantage over classical methods in past benchmarking studies. Recent advancements in QA with more than 5,000 qubits, enhanced qubit connectivity, and the hybrid architecture promise to realize the quantum advantage. Here, we use a quantum annealer with state-of-the-art techniques and benchmark its performance against classical solvers. To compare their performance, we solve over 50 optimization problem instances represented by large and dense Hamiltonian matrices using quantum and classical solvers. The results demonstrate that a state-of-the-art quantum solver has higher accuracy (~0.013%) and a significantly faster problem-solving time (~6,561x) than the best classical solver. Our results highlight the advantages of leveraging QA over classical counterparts, particularly in hybrid configurations, for achieving high accuracy and substantially reduced problem solving time in large-scale real-world optimization problems. |
| 2025-04-08 | Coupling approaches with non-matching grids for classical linear elasticity and bond-based peridynamic models in 1D | Patrick Diehl, Emily Downing, Autumn Edwards et.al. | 2504.06093 | | | Abstract (click to expand)Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. To facilitate the coupling of the two models, non-matching grids are often desirable as nonlocal grids usually require a finer resolution than local grids. In that case, it is often convenient to resort to interpolation operators so that models can exchange information in the overlap regions when nodes from the two grids do not coincide. This paper studies three existing coupling approaches, namely 1) a method that enforces matching displacements in an overlap region, 2) a variant that enforces a constraint on the stresses instead, and 3) a method that considers a variable horizon in the vicinity of the interfaces. The effect of the interpolation order and of the grid ratio on the performance of the three coupling methods with non-matching grids is carefully studied on one-dimensional examples using polynomial manufactured solutions. The numerical results show that the degree of the interpolants should be chosen with care to avoid introducing additional modeling errors, or simply minimize these errors, in the coupling approach. |
| 2025-04-08 | MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning | Marco Hoffmann, Thomas Specht, Nicolas Hayer et.al. | 2504.05970 | | 6 pages, 2 figures | Abstract (click to expand)Machine learning (ML) enables the development of powerful methods for predicting thermophysical properties with unprecedented scope and accuracy. However, technical barriers like cumbersome implementation in established workflows hinder their application in practice. With MLPROP, we provide an interactive web interface for directly applying advanced ML methods to predict thermophysical properties without requiring ML expertise, thereby substantially increasing the accessibility of novel models. MLPROP currently includes models for predicting the vapor pressure of pure components (GRAPPA), activity coefficients and vapor-liquid equilibria in binary mixtures (UNIFAC 2.0, mod. UNIFAC 2.0, and HANNA), and a routine to fit NRTL parameters to the model predictions. MLPROP will be continuously updated and extended and is accessible free of charge via https://ml-prop.mv.rptu.de/. MLPROP removes the barrier to learning and experimenting with new ML-based methods for predicting thermophysical properties. The source code of all models is available as open source, which allows integration into existing workflows. |
| 2025-04-08 | Physics-aware generative models for turbulent fluid flows through energy-consistent stochastic interpolants | Nikolaj T. Mücke, Benjamin Sanderse et.al. | 2504.05852 | link | | Abstract (click to expand)Generative models have demonstrated remarkable success in domains such as text, image, and video synthesis. In this work, we explore the application of generative models to fluid dynamics, specifically for turbulence simulation, where classical numerical solvers are computationally expensive. We propose a novel stochastic generative model based on stochastic interpolants, which enables probabilistic forecasting while incorporating physical constraints such as energy stability and divergence-freeness. Unlike conventional stochastic generative models, which are often agnostic to underlying physical laws, our approach embeds energy consistency by making the parameters of the stochastic interpolant learnable coefficients. We evaluate our method on a benchmark turbulence problem - Kolmogorov flow - demonstrating superior accuracy and stability over state-of-the-art alternatives such as autoregressive conditional diffusion models (ACDMs) and PDE-Refiner. Furthermore, we achieve stable results for significantly longer roll-outs than standard stochastic interpolants. Our results highlight the potential of physics-aware generative models in accelerating and enhancing turbulence simulations while preserving fundamental conservation properties. |
| 2025-04-07 | Deep Reinforcement Learning Algorithms for Option Hedging | Andrei Neagu, Frédéric Godin, Leila Kosseim et.al. | 2504.05521 | link | | Abstract (click to expand)Dynamic hedging is a financial strategy that consists in periodically transacting one or multiple financial assets to offset the risk associated with a correlated liability. Deep Reinforcement Learning (DRL) algorithms have been used to find optimal solutions to dynamic hedging problems by framing them as sequential decision-making problems. However, most previous work assesses the performance of only one or two DRL algorithms, making an objective comparison across algorithms difficult. In this paper, we compare the performance of eight DRL algorithms in the context of dynamic hedging; Monte Carlo Policy Gradient (MCPG), Proximal Policy Optimization (PPO), along with four variants of Deep Q-Learning (DQL) and two variants of Deep Deterministic Policy Gradient (DDPG). Two of these variants represent a novel application to the task of dynamic hedging. In our experiments, we use the Black-Scholes delta hedge as a baseline and simulate the dataset using a GJR-GARCH(1,1) model. Results show that MCPG, followed by PPO, obtain the best performance in terms of the root semi-quadratic penalty. Moreover, MCPG is the only algorithm to outperform the Black-Scholes delta hedge baseline with the allotted computational budget, possibly due to the sparsity of rewards in our environment. |
| 2025-04-07 | GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction | Yoshitaka Inoue, Tianfan Fu, Augustin Luna et.al. | 2504.05454 | | | Abstract (click to expand)Explainability is necessary for many tasks in biomedical research. Recent explainability methods have focused on attention, gradient, and Shapley value. These do not handle data with strong associated prior knowledge and fail to constrain explainability results based on known relationships between predictive features. We propose GraphPINE, a graph neural network (GNN) architecture leveraging domain-specific prior knowledge to initialize node importance optimized during training for drug response prediction. Typically, a manual post-prediction step examines literature (i.e., prior knowledge) to understand returned predictive features. While node importance can be obtained for gradient and attention after prediction, node importance from these methods lacks complementary prior knowledge; GraphPINE seeks to overcome this limitation. GraphPINE differs from other GNN gating methods by utilizing an LSTM-like sequential format. We introduce an importance propagation layer that unifies 1) updates for feature matrix and node importance and 2) uses GNN-based graph propagation of feature values. This initialization and updating mechanism allows for informed feature learning and improved graph representation. We apply GraphPINE to cancer drug response prediction using drug screening and gene data collected for over 5,000 gene nodes included in a gene-gene graph with a drug-target interaction (DTI) graph for initial importance. The gene-gene graph and DTIs were obtained from curated sources and weighted by article count discussing relationships between drugs and genes. GraphPINE achieves a PR-AUC of 0.894 and ROC-AUC of 0.796 across 952 drugs. Code is available at https://anonymous.4open.science/r/GraphPINE-40DE. |
| 2025-04-07 | A hydro-geomechanical porous-media model to study effects of engineered carbonate precipitation in faults | Yue Wang, Holger Class et.al. | 2504.05171 | | | Abstract (click to expand)Hydro-geomechanical models are required to predict or understand the impact of subsurface engineering applications as, for example, in gas storage in geological formations. This study puts a focus on engineered carbonate precipitation through biomineralization in a fault zone of a cap-rock to reduce gas leakage from a reservoir. Besides hydraulic properties like porosity and permeability, precipitated carbonates also change the mechanical properties of the rock. We present a conceptual modeling approach implemented into the open-source simulator Dumux and, after verification examples, at hand of a CO2-storage scenario, we discuss impacts of biomineralization on the stress distribution in the rock and potentially altered risks of fault reactivations and induced seismic events. The generic study shows the tendency towards increased stiffness due to precipitated carbonate, which may cause shear failure events to occur earlier than in an untreated setup, while the magnitude of the seismicity is smaller. |
| 2025-04-07 | Scaling Graph Neural Networks for Particle Track Reconstruction | Alok Tripathy, Alina Lazar, Xiangyang Ju et.al. | 2504.04670 | link | | Abstract (click to expand)Particle track reconstruction is an important problem in high-energy physics (HEP), necessary to study properties of subatomic particles. Traditional track reconstruction algorithms scale poorly with the number of particles within the accelerator. The Exa.TrkX project, to alleviate this computational burden, introduces a pipeline that reduces particle track reconstruction to edge classification on a graph, and uses graph neural networks (GNNs) to produce particle tracks. However, this GNN-based approach is memory-prohibitive and skips graphs that would exceed GPU memory. We introduce improvements to the Exa.TrkX pipeline to train on samples of input particle graphs, and show that these improvements generalize to higher precision and recall. In addition, we adapt performance optimizations, introduced for GNN training, to fit our augmented Exa.TrkX pipeline. These optimizations provide a \(2\times\) speedup over our baseline implementation in PyTorch Geometric. |
| 2025-04-06 | SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities | Noga Ben Yoash, Meni Brief, Oded Ovadia et.al. | 2504.04596 | | Benchmark available at: https://huggingface.co/datasets/nogabenyoash/SecQue | Abstract (click to expand)We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC filings analysis across four key categories: comparison analysis, ratio calculation, risk assessment, and financial insight generation. To assess model performance, we develop SECQUE-Judge, an evaluation mechanism leveraging multiple LLM-based judges, which demonstrates strong alignment with human evaluations. Additionally, we provide an extensive analysis of various models' performance on our benchmark. By making SECQUE publicly available, we aim to facilitate further research and advancements in financial AI. |
| 2025-04-06 | A model agnostic eXplainable AI based fuzzy framework for sensor constrained Aerospace maintenance applications | Bharadwaj Dogga, Anoop Sathyan, Kelly Cohen et.al. | 2504.04541 | | | Abstract (click to expand)Machine Learning methods have extensively evolved to support industrial big data methods and their corresponding need in gas turbine maintenance and prognostics. However, most unsupervised methods need extensively labeled data to perform predictions across many dimensions. The cutting edge of small and medium applications do not necessarily maintain operational sensors and data acquisition with rising costs and diminishing profits. We propose a framework to make sensor maintenance priority decisions using a combination of SHAP, UMAP, Fuzzy C-means clustering. An aerospace jet engine dataset is used as a case study. |
| 2025-04-06 | Robust and scalable nonlinear solvers for finite element discretizations of biological transportation networks | Jan Haskovec, Peter Markowich, Simone Portaro et.al. | 2504.04447 | | | Abstract (click to expand)We develop robust and scalable fully implicit nonlinear finite element solvers for the simulations of biological transportation networks driven by the gradient flow minimization of a non-convex energy cost functional. Our approach employs a discontinuous space for the conductivity tensor that allows us to guarantee the preservation of its positive semi-definiteness throughout the entire minimization procedure arising from the time integration of the gradient flow dynamics using a backward Euler scheme. Extensive tests in two and three dimensions demonstrate the robustness and performance of the solver, highlight the sensitivity of the emergent network structures to mesh resolution and topology, and validate the resilience of the linear preconditioner to the ill-conditioning of the model. The implementation achieves near-optimal parallel scaling on large-scale, high-performance computing platforms. To the best of our knowledge, the network formation system has never been simulated in three dimensions before. Consequently, our three-dimensional results are the first of their kind. |
| 2025-04-06 | From Coarse to Fine: A Physics-Informed Self-Guided Flow Diffusion Model | Ruoyan Li, Zijie Huang, Yizhou Sun et.al. | 2504.04375 | | | Abstract (click to expand)Machine learning methods are widely explored as a promising way to reconstruct high-fidelity computational fluid dynamics (CFD) data from faster-to-compute low-fidelity input. Diffusion models have achieved great success as they can reconstruct high-fidelity data from low-fidelity inputs at arbitrary resolution without re-training. However, most existing approaches assume that low-fidelity data is generated artificially via downsampling high-fidelity data. In reality, low-fidelity data is produced by numerical solvers that use a coarser resolution from the start, leading to substantial differences compared to high-fidelity data, especially in the long-range. Solver-generated low-fidelity data usually sacrifices fine-grained details, such as small-scale vortices compared to high-fidelity ones. To bridge this gap, we propose \model, a novel diffusion model for reconstruction, where both low- and high-fidelity data are straight from numerical solvers. Our findings show that state-of-the-art models struggle to generate fine-scale details when faced with solver-generated low-fidelity inputs. To address this challenge, we propose an \textit{Importance Weight} strategy during training that serves as a form of self-guidance, along with a training-free \textit{Residual Correction} approach during inference that embeds physical insights into the model. Together, these techniques steer the diffusion model toward more accurate reconstructions. Experimental results on four 2D turbulent flow datasets demonstrate the efficacy of our proposed method. |
| 2025-04-05 | Dynamic Hedging Strategies in Derivatives Markets with LLM-Driven Sentiment and News Analytics | Jie Yang, Yiqiu Tang, Yongjie Li et.al. | 2504.04295 | | Accepted by IJCNN 2025 | Abstract (click to expand)Dynamic hedging strategies are essential for effective risk management in derivatives markets, where volatility and market sentiment can greatly impact performance. This paper introduces a novel framework that leverages large language models (LLMs) for sentiment analysis and news analytics to inform hedging decisions. By analyzing textual data from diverse sources like news articles, social media, and financial reports, our approach captures critical sentiment indicators that reflect current market conditions. The framework allows for real-time adjustments to hedging strategies, adapting positions based on continuous sentiment signals. Backtesting results on historical derivatives data reveal that our dynamic hedging strategies achieve superior risk-adjusted returns compared to conventional static approaches. The incorporation of LLM-driven sentiment analysis into hedging practices presents a significant advancement in decision-making processes within derivatives trading. This research showcases how sentiment-informed dynamic hedging can enhance portfolio management and effectively mitigate associated risks. |
| 2025-04-05 | Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets | Jie Yang, Yiqiu Tang, Yongjie Li et.al. | 2504.04292 | | Accepted by IJCNN 2025 | Abstract (click to expand)Large language models (LLMs) have emerged as powerful tools in the field of finance, particularly for risk management across different asset classes. In this work, we introduce a Cross-Asset Risk Management framework that utilizes LLMs to facilitate real-time monitoring of equity, fixed income, and currency markets. This innovative approach enables dynamic risk assessment by aggregating diverse data sources, ultimately enhancing decision-making processes. Our model effectively synthesizes and analyzes market signals to identify potential risks and opportunities while providing a holistic view of asset classes. By employing advanced analytics, we leverage LLMs to interpret financial texts, news articles, and market reports, ensuring that risks are contextualized within broader market narratives. Extensive backtesting and real-time simulations validate the framework, showing increased accuracy in predicting market shifts compared to conventional methods. The focus on real-time data integration enhances responsiveness, allowing financial institutions to manage risks adeptly under varying market conditions and promoting financial stability through the advanced application of LLMs in risk analysis. |
| 2025-04-04 | A Unit-Cell Shape Optimization Approach for Maximizing Heat Transfer in Periodic Fin Arrays at Constant Solid Temperature | Maarten Blommaert, Arthur Vangeffelen, Mehmet Basaran et.al. | 2504.03436 | | Submitted to Structural and Multidisciplinary Optimization | Abstract (click to expand)Periodic fin structures are often employed to enhance heat transfer in compact cooling solutions and heat exchangers. Adjoint-based optimization methods are able to further increase the heat transfer by optimizing the fin geometry. However, obtaining optimal geometries remains challenging in general because of the high computational cost of full array simulations. In this paper, a unit cell optimization approach is presented that starts from recently developed macro-scale models for isothermal solid structures. The models exploit the periodicity of the problem to reduce the computational cost of evaluating the array heat transfer to that of a single periodic unit cell. By combining these models with a geometrically-constrained free-shape optimization approach, optimal fin geometries are obtained for the periodic fin array that maintain a minimal fin distance. Moreover, using an augmented Lagrangian approach, also the average pressure gradient and barycenter of the fin can be fixed. On a fictitious use-case, heat transfer increases up to 104 \% are obtained. When also flow rate is constrained in addition to maintain a high effectiveness, only up to 8 \% heat transfer increase is observed. Finally, the errors of the unit-cell optimization approach are investigated, indicating that with a good choice of cost functional formulation, errors of the approach as low as 1-2 \% can be obtained for the periodically developed part of the array. Finally, the entrance effect to the heat transfer is found to be non-negligible with a contribution of 10-15 \% for the considered fin array. This advocates for further research to extend the unit-cell models towards improved modeling of entrance effects. |
| 2025-04-04 | Optimal Sizing and Material Choice for Additively Manufactured Compact Plate Heat Exchangers | Mehmet Basaran, Frederik Rogiers, Martine Baelmans et.al. | 2504.03372 | | | Abstract (click to expand)With advancements in additive manufacturing (AM) capabilities, new opportunities arise to design compact heat exchangers (cHEXs) that leverage AM's degrees of freedom (DOFs) to enhance energy and material efficiency. However, excessive size reduction in counterflow cHEXs can compromise effectiveness due to axial heat conduction through the solid material, influenced by thermal conductivity and wall thickness. This study investigates how AM material selection and thin-wall production limitations might constrain the core size of counterflow plate heat exchangers when targeting maximum power density. An optimization framework evaluates power densities for six materials: plastic, austenitic steel, Al2O3, AlN, aluminum, and copper. Evaluations are conducted under constant effectiveness and pressure drop while accounting for AM-specific plate thickness limits and a lower bound on plate spacing to address fouling. Across all scenarios, copper cHEXs exhibit the lowest power density, despite high thermal conductivity. Without constraints on plate thickness and spacing, the optimal plastic cHEX achieves a power density 1800x greater than the steel baseline, while copper decreases by a factor of 0.98. With equal plate thickness of 0.5 mm for all materials, plastic retains the highest power density, 12.2x more than copper. Introducing a fouling constraint of 0.8 mm plate spacing shifts the optimal material to austenitic steel. When material-specific plate thicknesses are considered, the plastic cHEX achieves the highest power density, five times greater than copper, due to superior thin-wall resolution. This study highlights the impact of AM constraints on the energy and material efficiency of cHEXs, and shows that low-conductivity materials like plastic or austenitic steel can outperform high-conductivity materials like copper in compact designs. |
| 2025-04-03 | Adaptive Finite State Projection with Quantile-Based Pruning for Solving the Chemical Master Equation | Aditya Dendukuri, Linda Petzold et.al. | 2504.03070 | | | Abstract (click to expand)We present an adaptive Finite State Projection (FSP) method for efficiently solving the Chemical Master Equation (CME) with rigorous error control. Our approach integrates time-stepping with dynamic state-space truncation, balancing accuracy and computational cost. Krylov subspace methods approximate the matrix exponential, while quantile-based pruning controls state-space growth by removing low-probability states. Theoretical error bounds ensure that the truncation error remains bounded by the pruned mass at each step, which is user-controlled, and does not propagate forward in time. Numerical experiments on biochemical systems, including the Lotka-Volterra and Michaelis-Menten and bi-stable toggle switch models. |
| 2025-04-03 | Atrial constitutive neural networks | Mathias Peirlinck, Kevin Linka, Ellen Kuhl et.al. | 2504.02748 | | | Abstract (click to expand)This work presents a novel approach for characterizing the mechanical behavior of atrial tissue using constitutive neural networks. Based on experimental biaxial tensile test data of healthy human atria, we automatically discover the most appropriate constitutive material model, thereby overcoming the limitations of traditional, pre-defined models. This approach offers a new perspective on modeling atrial mechanics and is a significant step towards improved simulation and prediction of cardiac health. |
| 2025-04-03 | Grammar-based Ordinary Differential Equation Discovery | Karin L. Yu, Eleni Chatzi, Georgios Kissas et.al. | 2504.02630 | | | Abstract (click to expand)The understanding and modeling of complex physical phenomena through dynamical systems has historically driven scientific progress, as it provides the tools for predicting the behavior of different systems under diverse conditions through time. The discovery of dynamical systems has been indispensable in engineering, as it allows for the analysis and prediction of complex behaviors for computational modeling, diagnostics, prognostics, and control of engineered systems. Joining recent efforts that harness the power of symbolic regression in this domain, we propose a novel framework for the end-to-end discovery of ordinary differential equations (ODEs), termed Grammar-based ODE Discovery Engine (GODE). The proposed methodology combines formal grammars with dimensionality reduction and stochastic search for efficiently navigating high-dimensional combinatorial spaces. Grammars allow us to seed domain knowledge and structure for both constraining, as well as, exploring the space of candidate expressions. GODE proves to be more sample- and parameter-efficient than state-of-the-art transformer-based models and to discover more accurate and parsimonious ODE expressions than both genetic programming- and other grammar-based methods for more complex inference tasks, such as the discovery of structural dynamics. Thus, we introduce a tool that could play a catalytic role in dynamics discovery tasks, including modeling, system identification, and monitoring tasks. |
| 2025-04-03 | A Multi-Level Sentiment Analysis Framework for Financial Texts | Yiwei Liu, Junbo Wang, Lei Long et.al. | 2504.02429 | link | 14pages, 11 figures, 8 tables | Abstract (click to expand)Existing financial sentiment analysis methods often fail to capture the multi-faceted nature of risk in bond markets due to their single-level approach and neglect of temporal dynamics. We propose Multi-Level Sentiment Analysis based on pre-trained language models (PLMs) and large language models (LLMs), a novel framework that systematically integrates firm-specific micro-level sentiment, industry-specific meso-level sentiment, and duration-aware smoothing to model the latency and persistence of textual impact. Applying our framework to the comprehensive Chinese bond market corpus constructed by us (2013-2023, 1.39M texts), we extracted a daily composite sentiment index. Empirical results show statistically measurable improvements in credit spread forecasting when incorporating sentiment (3.25% MAE and 10.96% MAPE reduction), with sentiment shifts closely correlating with major social risk events and firm-specific crises. This framework provides a more nuanced understanding of sentiment across different market levels while accounting for the temporal evolution of sentiment effects. |
| 2025-04-03 | Solving adhesive rough contact problems with Atomic Force Microscope data | Maria Rosaria Marulli, Jacopo Bonari, Pasqualantonio Pingue et.al. | 2504.02307 | | | Abstract (click to expand)This study presents an advanced numerical framework that integrates experimentally acquired Atomic Force Microscope (AFM) data into high-fidelity simulations for adhesive rough contact problems, bridging the gap between experimental physics and computational mechanics. The proposed approach extends the eMbedded Profile for Joint Roughness (MPJR) interface finite element method to incorporate both surface topography and spatially varying adhesion properties, imported directly from AFM measurements. The adhesion behavior is modeled using a modified Lennard-Jones potential, which is locally parameterized based on the AFM-extracted adhesion peak force and energy dissipation data. The effectiveness of this method is demonstrated through 2D and 3D finite element simulations of a heterogeneous PS-LDPE (polystyrene matrix with low-density polyethylene inclusions) sample, where the bulk elastic properties are also experimentally characterized via AFM. The results highlight the significance of accounting for both surface adhesion variability and material bulk heterogeneity in accurately predicting contact responses. |
| 2025-04-03 | Parallel Market Environments for FinRL Contests | Keyi Wang, Kairong Xiao, Xiao-Yang Liu Yanglet et.al. | 2504.02281 | | | Abstract (click to expand)Reinforcement learning has shown great potential in finance. We have organized the FinRL Contests 2023-2025 featuring different financial tasks. Large language models have a strong capability to process financial texts. Integrating LLM-generated signals into FinRL is a new task, enabling agents to use both structured market data and unstructured financial text. To address the sampling bottleneck during training, we introduce GPU-based parallel market environments to improve sampling speed. In this paper, we summarize the parallel market environments used in FinRL Contests 2023-2025. Two new environments incorporate LLM-generated signals and support massively parallel simulation. Contestants utilize these environments to train agents for stock and cryptocurrency trading tasks. |
| 2025-04-04 | Stock Price Prediction Using Triple Barrier Labeling and Raw OHLCV Data: Evidence from Korean Markets | Sungwoo Kang et.al. | 2504.02249 | | 7 pages, 2 figures | Abstract (click to expand)This paper demonstrates that deep learning models trained on raw OHLCV (open-high-low-close-volume) data can achieve comparable performance to traditional machine learning (ML) models using technical indicators for stock price prediction in Korean markets. While previous studies have emphasized the importance of technical indicators and feature engineering, we show that a simple LSTM network trained on raw OHLCV data alone can match the performance of sophisticated ML models that incorporate technical indicators. Using a dataset of Korean stocks from 2006 to 2024, we optimize the triple barrier labeling parameters to achieve balanced label proportions with a 29-day window and 9\% barriers. Our experiments reveal that LSTM networks achieve similar performance to traditional machine learning models like XGBoost, despite using only raw OHLCV data without any technical indicators. Furthermore, we identify that the optimal window size varies with model hidden size, with a configuration of window size 100 and hidden size 8 yielding the best performance. Additionally, our results confirm that using full OHLCV data provides better predictive accuracy compared to using only close price or close price with volume. These findings challenge conventional approaches to feature engineering in financial forecasting and suggest that simpler approaches focusing on raw data and appropriate model selection may be more effective than complex feature engineering strategies. |
| 2025-04-04 | A User-Tunable Machine Learning Framework for Step-Wise Synthesis Planning | Shivesh Prakash, Hans-Arno Jacobsen, Viki Kumar Prasad et.al. | 2504.02191 | link | | Abstract (click to expand)We introduce MHNpath, a machine learning-driven retrosynthetic tool designed for computer-aided synthesis planning. Leveraging modern Hopfield networks and novel comparative metrics, MHNpath efficiently prioritizes reaction templates, improving the scalability and accuracy of retrosynthetic predictions. The tool incorporates a tunable scoring system that allows users to prioritize pathways based on cost, reaction temperature, and toxicity, thereby facilitating the design of greener and cost-effective reaction routes. We demonstrate its effectiveness through case studies involving complex molecules from ChemByDesign, showcasing its ability to predict novel synthetic and enzymatic pathways. Furthermore, we benchmark MHNpath against existing frameworks, replicating experimentally validated "gold-standard" pathways from PaRoutes. Our case studies reveal that the tool can generate shorter, cheaper, moderate-temperature routes employing green solvents, as exemplified by compounds such as dronabinol, arformoterol, and lupinine. |
| 2025-04-02 | A Quality Diversity Approach to Evolving Model Rockets | Jacob Schrum, Cody Crosby et.al. | 2504.02177 | link | In Genetic and Evolutionary Computation Conference (GECCO '25), July 14-18, 2025, Malaga, Spain. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3712256.3726338 | Abstract (click to expand)Model rocketry presents a design task accessible to undergraduates while remaining an interesting challenge. Allowing for variation in fins, nose cones, and body tubes presents a rich design space containing numerous ways to achieve various altitudes. Therefore, when exploring possible designs computationally, it makes sense to apply a method that produces various possibilities for decision-makers to choose from: Quality Diversity (QD). The QD methods MAP-Elites, CMA-ME, and CMA-MAE are applied to model rocket design using the open-source OpenRocket software to characterize the behavior and determine the fitness of evolved designs. Selected rockets were manufactured and launched to evaluate them in the real world. Simulation results demonstrate that CMA-ME produces the widest variety of rocket designs, which is surprising given that CMA-MAE is a more recent method designed to overcome shortcomings with CMA-ME. Real-world testing demonstrates that a wide range of standard and unconventional designs are viable, though issues with the jump from simulation to reality cause some rockets to perform unexpectedly. This paper provides a case study on applying QD to a task accessible to a broader audience than industrial engineering tasks and uncovers unexpected results about the relative performance of different QD algorithms. |
| 2025-04-02 | Responsible Innovation: A Strategic Framework for Financial LLM Integration | Ahmadreza Tavasoli, Maedeh Sharbaf, Seyed Mohamad Madani et.al. | 2504.02165 | | 27 pages, 3 figures | Abstract (click to expand)Financial institutions of all sizes are increasingly adopting Large Language Models (LLMs) to enhance credit assessments, deliver personalized client advisory services, and automate various language-intensive processes. However, effectively deploying LLMs requires careful management of stringent data governance requirements, heightened demands for interpretability, ethical responsibilities, and rapidly evolving regulatory landscapes. To address these challenges, we introduce a structured six-decision framework specifically designed for the financial sector, guiding organizations systematically from initial feasibility assessments to final deployment strategies. The framework encourages institutions to: (1) evaluate whether an advanced LLM is necessary at all, (2) formalize robust data governance and privacy safeguards, (3) establish targeted risk management mechanisms, (4) integrate ethical considerations early in the development process, (5) justify the initiative's return on investment (ROI) and strategic value, and only then (6) choose the optimal implementation pathway -- open-source versus proprietary, or in-house versus vendor-supported -- aligned with regulatory requirements and operational realities. By linking strategic considerations with practical steps such as pilot testing, maintaining comprehensive audit trails, and conducting ongoing compliance evaluations, this decision framework offers a structured roadmap for responsibly leveraging LLMs. Rather than acting as a rigid, one-size-fits-all solution, it shows how advanced language models can be thoughtfully integrated into existing workflows -- balancing innovation with accountability to uphold stakeholder trust and regulatory integrity. |
| 2025-04-02 | Vectorised Parallel in Time methods for low-order discretizations with application to Porous Media problems | Christian Engwer, Alexander Schell, Nils-Arne Dreier et.al. | 2504.02117 | | | Abstract (click to expand)High order methods have shown great potential to overcome performance issues of simulations of partial differential equations (PDEs) on modern hardware, still many users stick to low-order, matrixbased simulations, in particular in porous media applications. Heterogeneous coefficients and low regularity of the solution are reasons not to employ high order discretizations. We present a new approach for the simulation of instationary PDEs that allows to partially mitigate the performance problems. By reformulating the original problem we derive a parallel in time time integrator that increases the arithmetic intensity and introduces additional structure into the problem. By this it helps accelerate matrix-based simulations on modern hardware architectures. Based on a system for multiple time steps we will formulate a matrix equation that can be solved using vectorised solvers like Block Krylov methods. The structure of this approach makes it applicable for a wide range of linear and nonlinear problems. In our numerical experiments we present some first results for three different PDEs, a linear convection-diffusion equation, a nonlinear diffusion-reaction equation and a realistic example based on the Richards' equation. |
| 2025-04-02 | Focal Mechanism Uncertainty Quantification In Ground Motion Simulations Of Le Teil Earthquake | Valeria Soto, Fernando Lopez-Caballero et.al. | 2504.01868 | | | Abstract (click to expand)Ensuring the seismic safety of nuclear power plants (NPPs) is essential, especially for facilities that rely on base isolation to reduce earthquake impacts. For understanding the seismic response, accurate models are key to predict the ground motions, which are generally sensitive to various factors, including earthquake source parameters like the focal mechanism, i.e., strike, dip, and rake angles. This study examines how uncertainties in these parameters affect ground motion predictions. The analysis is based on the SMATCH benchmark, which provides a standardized approach for evaluating the seismic response of the Cruas-Meysse NPP in France during the Mw 4.9 Le-Teil earthquake of 2019. A set of 27 3D high-fidelity numerical simulations was performed using a spectral-element method, each incorporating different focal mechanism variations. These simulations provide an effective approach for investigating the factors behind the exceptional ground motion observed during this event. To quantify uncertainty, the simulated ground motions were compared to recorded data using two well-established goodness-of-fit criteria: one assessing time-frequency domain characteristics and another focusing on the characterization of the ground motion signals by intensity measures. Results highlight the significant influence of focal mechanism variability on ground motion predictions, especially on the rake angle, which showed the strongest correlation with wave and intensity measures. |
| 2025-04-02 | Design and Experimental Validation of an Urban Microclimate Tool Integrating Indoor-Outdoor Detailed Longwave Radiative Fluxes at District Scale | Marie-Hélène Azam, Julien Berger, Edouard Walther et.al. | 2504.01736 | | | Abstract (click to expand)Numerical simulation is a powerful tool for assessing the causes of an Urban Heat Island (UHI) effect or quantifying the impact of mitigation solutions on outdoor and indoor thermal comfort. For that purpose, several models have been developed at the district scale. At this scale, the outside surface energy budget is detailed, however building models are very simplified and considered as a boundary condition of the district scale model. This shortcoming inhibits the opportunity to investigate the effect of urban microclimate on the inside building conditions. The aim of this work is to improve the representation of the physical phenomena involved in the building models of a district model. For that purpose, the model integrates inside and outside fully detailed long-wave radiative flux. The numerical model is based on finite differences to solve conduction through all the surfaces and the radiosity method to solve long-wave radiative heat fluxes inside and outside. Calculated temperatures and heat fluxes are evaluated with respect to \textit{in situ} measurements from an experimental demonstrator over 14 sensors and a 24-day period. Results are also compared to state-of-the-art models simulation tool show improvement of the RMSE of \(0.9 \ \mathsf{^{\,\circ}C}\) to \(2.1 \ \mathsf{^{\,\circ}C}\) on the surface temperature modeled. |
| 2025-04-02 | Anomaly Detection for Hybrid Butterfly Subspecies via Probability Filtering | Bo-Kai Ruan, Yi-Zeng Fang, Hong-Han Shuai et.al. | 2504.01671 | link | AAAI'25 Workshop in Anomaly Detection in Scientific Domains | Abstract (click to expand)Detecting butterfly hybrids requires knowledge of the parent subspecies, and the process can be tedious when encountering a new subspecies. This study focuses on a specific scenario where a model trained to recognize hybrid species A can generalize to species B when B biologically mimics A. Since species A and B share similar patterns, we leverage BioCLIP as our feature extractor to capture features based on their taxonomy. Consequently, the algorithm designed for species A can be transferred to B, as their hybrid and non-hybrid patterns exhibit similar relationships. To determine whether a butterfly is a hybrid, we adopt proposed probability filtering and color jittering to augment and simulate the mimicry. With these approaches, we achieve second place in the official development phase. Our code is publicly available at https://github.com/Justin900429/NSF-HDR-Challenge. |
| 2025-04-02 | A computational framework for evaluating tire-asphalt hysteretic friction including pavement roughness | Ivana Ban, Jacopo Bonari, Marco Paggi et.al. | 2504.01511 | | | Abstract (click to expand)Pavement surface textures obtained by a photogrammetry-based method for data acquisition and analysis are employed to investigate if related roughness descriptors are comparable to the frictional performance evaluated by finite element analysis. Pavement surface profiles are obtained from 3D digital surface models created with Close-Range Orthogonal Photogrammetry. To characterize the roughness features of analyzed profiles, selected texture parameters were calculated from the profile's geometry. The parameters values were compared to the frictional performance obtained by numerical simulations. Contact simulations are performed according to a dedicated finite element scheme where surface roughness is directly embedded into a special class of interface finite elements. Simulations were performed for different case scenarios and the obtained results showed a notable trend between roughness descriptors and friction performance, indicating a promising potential for this numerical method to be consistently employed to predict the frictional properties of actual pavement surface profiles. |
| 2025-04-02 | Multi-convex Programming for Discrete Latent Factor Models Prototyping | Hao Zhu, Shengchao Yan, Jasper Hoffmann et.al. | 2504.01431 | link | | Abstract (click to expand)Discrete latent factor models (DLFMs) are widely used in various domains such as machine learning, economics, neuroscience, psychology, etc. Currently, fitting a DLFM to some dataset relies on a customized solver for individual models, which requires lots of effort to implement and is limited to the targeted specific instance of DLFMs. In this paper, we propose a generic framework based on CVXPY, which allows users to specify and solve the fitting problem of a wide range of DLFMs, including both regression and classification models, within a very short script. Our framework is flexible and inherently supports the integration of regularization terms and constraints on the DLFM parameters and latent factors, such that the users can easily prototype the DLFM structure according to their dataset and application scenario. We introduce our open-source Python implementation and illustrate the framework in several examples. |
| 2025-04-02 | Accelerating Blockchain Scalability: New Models for Parallel Transaction Execution in the EVM | Souradeep Das, Konpat Preechakul, Jonas Bäumer et.al. | 2504.01370 | | | Abstract (click to expand)As the number of decentralized applications and users on Ethereum grows, the ability of the blockchain to efficiently handle a growing number of transactions becomes increasingly strained. Ethereums current execution model relies heavily on sequential processing, meaning that operations are processed one after the other, which creates significant bottlenecks to future scalability demands. While scalability solutions for Ethereum exist, they inherit the limitations of the EVM, restricting the extent to which they can scale. This paper proposes a novel solution to enable maximally parallelizable executions within Ethereum, built out of three self-sufficient approaches. These approaches include strategies in which Ethereum transaction state accesses could be strategically and efficiently predetermined, and further propose how the incorporation of gas based incentivization mechanisms could enforce a maximally parallelizable network. |
| 2025-04-01 | A batch production scheduling problem in a reconfigurable hybrid manufacturing-remanufacturing system | Behdin Vahedi-Nouria, Mohammad Rohaninejad, Zdeněk Hanzálek et.al. | 2504.00605 | | | Abstract (click to expand)In recent years, remanufacturing of End-of-Life (EOL) products has been adopted by manufacturing sectors as a competent practice to enhance their sustainability, resiliency, and market share. Due to the mass customization of products and high volatility of market, processing of new products and remanufacturing of EOLs in a same shared facility, namely Hybrid Manufacturing-Remanufacturing System (HMRS), is a mean to keep such production efficient. Accordingly, customized production capabilities are required to increase flexibility, which can be suitably provided under the Reconfigurable Manufacturing System (RMS) paradigm. Despite the advantages of utilizing RMS technologies in HMRSs, production management of such systems suffers excessive complexity. Hence, this study concentrates on the production scheduling of an HMRS consisting of non-identical parallel reconfigurable machines where the orders can be grouped into batches. In this regard, Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) models are devised to formulate the problem. Furthermore, an efficient solution method is developed based on a Logic-based Benders Decomposition (LBBD) approach. The warm start technique is also implemented by providing a decent initial solution to the MILP model. Computational experiments attest to the LBBD method's superiority over the MILP, CP, and warm started MILP models by obtaining an average gap of about 2%, besides it provides valuable managerial insights. |
| 2025-04-01 | Towards Calibrating Financial Market Simulators with High-frequency Data | Peng Yang, Junji Ren, Feng Wang et.al. | 2504.00538 | | | Abstract (click to expand)The fidelity of financial market simulation is restricted by the so-called "non-identifiability" difficulty when calibrating high-frequency data. This paper first analyzes the inherent loss of data information in this difficulty, and proposes to use the Kolmogorov-Smirnov test (K-S) as the objective function for high-frequency calibration. Empirical studies verify that K-S has better identifiability of calibrating high-frequency data, while also leads to a much harder multi-modal landscape in the calibration space. To this end, we propose the adaptive stochastic ranking based negatively correlated search algorithm for improving the balance between exploration and exploitation. Experimental results on both simulated data and real market data demonstrate that the proposed method can obtain up to 36.0% improvement in high-frequency data calibration problems over the compared methods. |
| 2025-04-01 | Carbon and Reliability-Aware Computing for Heterogeneous Data Centers | Yichao Zhang, Yubo Song, Subham Sahoo et.al. | 2504.00518 | | The manuscript has been submitted for review to IEEE Transactions on Smart Grid | Abstract (click to expand)The rapid expansion of data centers (DCs) has intensified energy and carbon footprint, incurring a massive environmental computing cost. While carbon-aware workload migration strategies have been examined, existing approaches often overlook reliability metrics such as server lifetime degradation, and quality-of-service (QoS) that substantially affects both carbon and operational efficiency of DCs. Hence, this paper proposes a comprehensive optimization framework for spatio-temporal workload migration across distributed DCs that jointly minimizes operational and embodied carbon emissions while complying with service-level agreements (SLA). A key contribution is the development of an embodied carbon emission model based on servers' expected lifetime analysis, which explicitly considers server heterogeneity resulting from aging and utilization conditions. These issues are accommodated using new server dispatch strategies, and backup resource allocation model, accounting hardware, software and workload-induced failure. The overall model is formulated as a mixed-integer optimization problem with multiple linearization techniques to ensure computational tractability. Numerical case studies demonstrate that the proposed method reduces total carbon emissions by up to 21%, offering a pragmatic approach to sustainable DC operations. |
| 2025-04-01 | Aggregate Flexibility of Thermostatically Controlled Loads using Generalized Polymatroids | Karan Mukhi, Alessandro Abate et.al. | 2504.00484 | | | Abstract (click to expand)Leveraging populations of thermostatically controlled loads could provide vast storage capacity to the grid. To realize this potential, their flexibility must be accurately aggregated and represented to the system operator as a single, controllable virtual device. Mathematically this is computed by calculating the Minkowski sum of the individual flexibility of each of the devices. Previous work showed how to exactly characterize the flexibility of lossless storage devices as generalized polymatroids-a family of polytope that enable an efficient computation of the Minkowski sum. In this paper we build on these results to encompass devices with dissipative storage dynamics. In doing so we are able to provide tractable methods of accurately characterizing the flexibility in populations consisting of a variety of heterogeneous devices. Numerical results demonstrate that the proposed characterizations are tight. |
| 2025-04-01 | Anisotropic mesh spacing prediction using neural networks | Callum Lock, Oubay Hassan, Ruben Sevilla et.al. | 2504.00456 | | 30 pages, 16 figures | Abstract (click to expand)This work presents a framework to predict near-optimal anisotropic spacing functions suitable to perform simulations with unseen operating conditions or geometric configurations. The strategy consists of utilising the vast amount of high fidelity data available in industry to compute a target anisotropic spacing and train an artificial neural network to predict the spacing for unseen scenarios. The trained neural network outputs the metric tensor at the nodes of a coarse background mesh that is then used to generate meshes for unseen cases. Examples are used to demonstrate the effect of the network hyperparameters and the training dataset on the accuracy of the predictions. The potential is demonstrated for examples involving up to 11 geometric parameters on CFD simulations involving a full aircraft configuration. |
| 2025-04-01 | Transfer Learning in Financial Time Series with Gramian Angular Field | Hou-Wan Long, On-In Ho, Qi-Qiao He et.al. | 2504.00378 | | | Abstract (click to expand)In financial analysis, time series modeling is often hampered by data scarcity, limiting neural network models' ability to generalize. Transfer learning mitigates this by leveraging data from similar domains, but selecting appropriate source domains is crucial to avoid negative transfer. This study enhances source domain selection in transfer learning by introducing Gramian Angular Field (GAF) transformations to improve time series similarity functions. We evaluate a comprehensive range of baseline similarity functions, including both basic and state-of-the-art (SOTA) functions, and perform extensive experiments with Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) networks. The results demonstrate that GAF-based similarity functions significantly reduce prediction errors. Notably, Coral (GAF) for DNN and CMD (GAF) for LSTM consistently deliver superior performance, highlighting their effectiveness in complex financial environments. |
| 2025-03-31 | Graph Neural Network-Based Predictive Modeling for Robotic Plaster Printing | Diego Machain Rivera, Selen Ercan Jenny, Ping Hsun Tsai et.al. | 2503.24130 | | | Abstract (click to expand)This work proposes a Graph Neural Network (GNN) modeling approach to predict the resulting surface from a particle based fabrication process. The latter consists of spray-based printing of cementitious plaster on a wall and is facilitated with the use of a robotic arm. The predictions are computed using the robotic arm trajectory features, such as position, velocity and direction, as well as the printing process parameters. The proposed approach, based on a particle representation of the wall domain and the end effector, allows for the adoption of a graph-based solution. The GNN model consists of an encoder-processor-decoder architecture and is trained using data from laboratory tests, while the hyperparameters are optimized by means of a Bayesian scheme. The aim of this model is to act as a simulator of the printing process, and ultimately used for the generation of the robotic arm trajectory and the optimization of the printing parameters, towards the materialization of an autonomous plastering process. The performance of the proposed model is assessed in terms of the prediction error against unseen ground truth data, which shows its generality in varied scenarios, as well as in comparison with the performance of an existing benchmark model. The results demonstrate a significant improvement over the benchmark model, with notably better performance and enhanced error scaling across prediction steps. |
| 2025-03-31 | Organizations, teams, and job mobility: A social microdynamics approach | Bryan Adams, Valentín Vergara Hidd, Daniel Stimpson et.al. | 2503.24117 | | | Abstract (click to expand)The internal structures of large organizations determine much of what occurs inside including the way in which tasks are performed, the workers that perform them, and the mobility of those workers within the organization. However, regarding this latter process, most of the theoretical and modeling approaches used to understand organizational worker mobility are highly stylized, using idealizations such as structureless organizations, indistinguishable workers, and a lack of social bonding of the workers. In this article, aided by a decade of precise, temporally resolved data of a large US government organization, we introduce a new model to describe organizations as composites of teams within which individuals perform specific tasks and where social connections develop. By tracking the personnel composition of organizational teams, we find that workers that change jobs are highly influenced by preferring to reunite with past co-workers. In this organization, 34\% of all moves lead to worker reunions, a percentage well-above expectation. We find that the greater the time workers spend together or the smaller the team they share both increase their likelihood to reunite, supporting the notion of increased familiarity and trust behind such reunions and the dominant role of social capital in the evolution of large organizations. |
| 2025-03-31 | Estimation of thermal properties and boundary heat transfer coefficient of the ground with a Bayesian technique | Zhanat Karashbayeva, Julien Berger, Helcio R. B. Orlande et.al. | 2503.24072 | | | Abstract (click to expand)Urbanization is the key contributor for climate change. Increasing urbanization rate causes an urban heat island (UHI) effect, which strongly depends on the short- and long-wave radiation balance heat flux between the surfaces. In order to calculate accurately this heat flux, it is required to assess the surface temperature which depends on the knowledge of the thermal properties and the surface heat transfer coefficients in the heat transfer problem. The aim of this paper is to estimate the thermal properties of the ground and the time varying surface heat transfer coefficient by solving an inverse problem. The Dufort--Frankel scheme is applied for solving the unsteady heat transfer problem. For the inverse problem, a Markov chain Monte Carlo method is used to estimate the posterior probability density function of unknown parameters within the Bayesian framework of statistics, by applying the Metropolis-Hastings algorithm for random sample generation. Actual temperature measurements available at different ground depths were used for the solution of the inverse problem. Different time discretizations were examined for the transient heat transfer coefficient at the ground surface, which then involved different prior distributions. Results of different case studies show that the estimated values of the unknown parameters were in accordance with literature values. Moreover, with the present solution of the inverse problem the temperature residuals were smaller than those obtained by using literature values for the unknowns. |
| 2025-03-31 | Evaluating Variational Quantum Eigensolver and Quantum Dynamics Algorithms on the Advection-Diffusion Equation | A. Barış Özgüler et.al. | 2503.24045 | | 7 pages, 2 figures | Abstract (click to expand)We investigate the potential of near-term quantum algorithms for solving partial differential equations (PDEs), focusing on a linear one-dimensional advection-diffusion equation as a test case. This study benchmarks a ground-state algorithm, Variational Quantum Eigensolver (VQE), against three leading quantum dynamics algorithms, Trotterization, Variational Quantum Imaginary Time Evolution (VarQTE), and Adaptive Variational Quantum Dynamics Simulation (AVQDS), applied to the same PDE on small quantum hardware. While Trotterization is fully quantum, VarQTE and AVQDS are variational algorithms that reduce circuit depth for noisy intermediate-scale quantum (NISQ) devices. However, hardware results from these dynamics methods show sizable errors due to noise and limited shot statistics. To establish a noise-free performance baseline, we implement the VQE-based solver on a noiseless statevector simulator. Our results show VQE can reach final-time infidelities as low as \({O}(10^{-9})\) with \(N=4\) qubits and moderate circuit depths, outperforming hardware-deployed dynamics methods that show infidelities \(\gtrsim 10^{-2}\) . By comparing noiseless VQE to shot-based and hardware-run algorithms, we assess their accuracy and resource demands, providing a baseline for future quantum PDE solvers. We conclude with a discussion of limitations and potential extensions to higher-dimensional, nonlinear PDEs relevant to engineering and finance. |
| 2025-03-30 | Exact Characterization of Aggregate Flexibility via Generalized Polymatroids | Karan Mukhi, Georg Loho, Alessandro Abate et.al. | 2503.23458 | | | Abstract (click to expand)There is growing interest in utilizing the flexibility in populations of distributed energy resources (DER) to mitigate the intermittency and uncertainty of renewable generation and provide additional grid services. To enable this, aggregators must effectively represent the flexibility in the populations they control to the market or system operator. A key challenge is accurately computing the aggregate flexibility of a population, which can be formally expressed as the Minkowski sum of a collection of polytopes - a problem that is generally computationally intractable. However, the flexibility polytopes of many DERs exhibit structural symmetries that can be exploited for computational efficiency. To this end, we introduce generalized polymatroids - a family of polytope - into the flexibility aggregation literature. We demonstrate that individual flexibility sets belong to this family, enabling efficient computation of their Minkowski sum. For homogeneous populations of DERs we further derive simplifications that yield more succinct representations of aggregate flexibility. Additionally, we develop an efficient optimization framework over these sets and propose a vertex-based disaggregation method, to allocate aggregate flexibility among individual DERs. Finally, we validate the optimality and computational efficiency of our approach through comparisons with existing methods. |
| 2025-03-30 | AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design | Mohamed Elrefaie, Janet Qian, Raina Wu et.al. | 2503.23315 | | | Abstract (click to expand)We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process, while emphasizing that our approach can be readily extended to other engineering and design domains. Our framework integrates AI-driven design agents into the traditional engineering workflow, demonstrating how these specialized computational agents interact seamlessly with engineers and designers to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle. By automating and streamlining tasks traditionally performed manually, such as conceptual sketching, styling enhancements, 3D shape retrieval and generative modeling, computational fluid dynamics (CFD) meshing, and aerodynamic simulations, our approach reduces certain aspects of the conventional workflow from weeks and days down to minutes. These agents leverage state-of-the-art vision-language models (VLMs), large language models (LLMs), and geometric deep learning techniques, providing rapid iteration and comprehensive design exploration capabilities. We ground our methodology in industry-standard benchmarks, encompassing a wide variety of conventional automotive designs, and utilize high-fidelity aerodynamic simulations to ensure practical and applicable outcomes. Furthermore, we present design agents that can swiftly and accurately predict simulation outcomes, empowering engineers and designers to engage in more informed design optimization and exploration. This research underscores the transformative potential of integrating advanced generative AI techniques into complex engineering tasks, paving the way for broader adoption and innovation across multiple engineering disciplines. |
| 2025-03-29 | Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting | Eftychia Makri, Georgios Palaiokrassas, Sarah Bouraga et.al. | 2503.23190 | | | Abstract (click to expand)Cryptocurrencies have transformed financial markets with their innovative blockchain technology and volatile price movements, presenting both challenges and opportunities for predictive analytics. Ethereum, being one of the leading cryptocurrencies, has experienced significant market fluctuations, making its price prediction an attractive yet complex problem. This paper presents a comprehensive study on the effectiveness of Large Language Models (LLMs) in predicting Ethereum prices for short-term and few-shot forecasting scenarios. The main challenge in training models for time series analysis is the lack of data. We address this by leveraging a novel approach that adapts existing pre-trained LLMs on natural language or images from billions of tokens to the unique characteristics of Ethereum price time series data. Through thorough experimentation and comparison with traditional and contemporary models, our results demonstrate that selectively freezing certain layers of pre-trained LLMs achieves state-of-the-art performance in this domain. This approach consistently surpasses benchmarks across multiple metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), demonstrating its effectiveness and robustness. Our research not only contributes to the existing body of knowledge on LLMs but also provides practical insights in the cryptocurrency prediction domain. The adaptability of pre-trained LLMs to handle the nature of Ethereum prices suggests a promising direction for future research, potentially including the integration of sentiment analysis to further refine forecasting accuracy. |
| 2025-04-01 | HRET: A Self-Evolving LLM Evaluation Toolkit for Korean | Hanwool Lee, Soo Yong Kim, Dasol Choi et.al. | 2503.22968 | | | Abstract (click to expand)Recent advancements in Korean large language models (LLMs) have spurred numerous benchmarks and evaluation methodologies, yet the lack of a standardized evaluation framework has led to inconsistent results and limited comparability. To address this, we introduce HRET Haerae Evaluation Toolkit, an open-source, self-evolving evaluation framework tailored specifically for Korean LLMs. HRET unifies diverse evaluation methods, including logit-based scoring, exact-match, language-inconsistency penalization, and LLM-as-a-Judge assessments. Its modular, registry-based architecture integrates major benchmarks (HAE-RAE Bench, KMMLU, KUDGE, HRM8K) and multiple inference backends (vLLM, HuggingFace, OpenAI-compatible endpoints). With automated pipelines for continuous evolution, HRET provides a robust foundation for reproducible, fair, and transparent Korean NLP research. |
| 2025-03-28 | Co-design of materials, structures and stimuli for magnetic soft robots with large deformation and dynamic contacts | Liwei Wang et.al. | 2503.22767 | | | Abstract (click to expand)Magnetic soft robots embedded with hard magnetic particles enable untethered actuation via external magnetic fields, offering remote, rapid, and precise control, which is highly promising for biomedical applications. However, designing such systems is challenging due to the complex interplay of magneto-elastic dynamics, large deformation, solid contacts, time-varying stimuli, and posture-dependent loading. As a result, most existing research relies on heuristics and trial-and-error methods or focuses on the independent design of stimuli or structures under static conditions. We propose a topology optimization framework for magnetic soft robots that simultaneously designs structures, location-specific material magnetization and time-varying magnetic stimuli, accounting for large deformations, dynamic motion, and solid contacts. This is achieved by integrating generalized topology optimization with the magneto-elastic material point method, which supports GPU-accelerated parallel simulations and auto-differentiation for sensitivity analysis. We applied this framework to design magnetic robots for various tasks, including multi-task shape morphing and locomotion, in both 2D and 3D. The method autonomously generates optimized robotic systems to achieve target behaviors without requiring human intervention. Despite the nonlinear physics and large design space, it demonstrates exceptional efficiency, completing all cases within minutes. This proposed framework represents a significant step toward the automatic co-design of magnetic soft robots for applications such as metasurfaces, drug delivery, and minimally invasive procedures. |
| 2025-03-28 | A high order multigrid-preconditioned immersed interface solver for the Poisson equation with boundary and interface conditions | James Gabbard, Andrea Paris, Wim M. van Rees et.al. | 2503.22455 | | | Abstract (click to expand)This work presents a multigrid preconditioned high order immersed finite difference solver to accurately and efficiently solve the Poisson equation on complex 2D and 3D domains. The solver employs a low order Shortley-Weller multigrid method to precondition a high order matrix-free Krylov subspace solver. The matrix-free approach enables full compatibility with high order IIM discretizations of boundary and interface conditions, as well as high order wavelet-adapted multiresolution grids. Through verification and analysis on 2D domains, we demonstrate the ability of the algorithm to provide high order accurate results to Laplace and Poisson problems with Dirichlet, Neumann, and/or interface jump boundary conditions, all effectively preconditioned using the multigrid method. We further show that the proposed method is able to efficiently solve high order discretizations of Laplace and Poisson problems on complex 3D domains using thousands of compute cores and on multiresolution grids. To our knowledge, this work presents the largest problem sizes tackled with high order immersed methods applied to elliptic partial differential equations, and the first high order results on 3D multiresolution adaptive grids. Together, this work paves the way for employing high order immersed methods to a variety of 3D partial differential equations with boundary or inter-face conditions, including linear and non-linear elasticity problems, the incompressible Navier-Stokes equations, and fluid-structure interactions. |
| 2025-03-28 | Numerical optimization of aviation decarbonization scenarios: balancing traffic and emissions with maturing energy carriers and aircraft technology | Ian Costa-Alves, Nicolas Gourdain, François Gallard et.al. | 2503.22435 | link | | Abstract (click to expand)Despite being considered a hard-to-abate sector, aviation's emissions will play an important role in long-term climate mitigation of transportation. The introduction of low-carbon energy carriers and the deployment of new aircraft in the current fleet are modeled as a technology-centered decarbonization policy, and supply constraints in targeted market segments are modeled as demand-side policy. Shared socioeconomic pathways (SSP) are used to estimate the trend traffic demand and limit the sectoral consumption of electricity and biomass. Mitigation scenarios are formulated as optimization problems and three applications are demonstrated: single-policy optimization, scenario-robust policy, and multiobjective policy trade-off. Overall, we find that the choice of energy carrier to embark is highly dependent on assumptions regarding aircraft technology and background energy system, and that aligning trend scenarios with the Paris Agreement market-targeted traffic constraints are required to align trend scenarios with the Paris Agreement. The usual burdens associated with nonlinear optimization with high-dimensional variables are dealt with by jointly using libraries for Multidisciplinary Optimization (GEMSEO) and Automatic Differentiation (JAX), which resulted in speedups of two orders of magnitude at the optimization level, while reducing associated implementation efforts. |
| 2025-03-28 | Inverse design of dual-band valley-Hall topological photonic crystals with arbitrary pseudospin states | Yuki Sato, Shrinathan Esaki Muthu Pandara Kone, Junpei Oba et.al. | 2503.22206 | | 13 pages, 9 figures | Abstract (click to expand)Valley photonic crystals (VPCs) offer topological kink states that ensure robust, unidirectional, and backscattering-immune light propagation. The design of VPCs is typically based on analogies with condensed-matter topological insulators that exhibit the quantum valley Hall effect; trial-and-error approaches are often used to tailor the photonic band structures and their topological properties, which are characterized by the local Berry curvatures. In this paper, we present an inverse design framework based on frequency-domain analysis for VPCs with arbitrary pseudospin states. Specifically, we utilize the transverse spin angular momentum (TSAM) at the band edge to formulate the objective function for engineering the desired topological properties. Numerical experiments demonstrate that our proposed design approach can successfully produce photonic crystal waveguides exhibiting dual-band operation, enabling frequency-dependent light routing. Our pseudospin-engineering method thus provides a cost-effective alternative for designing topological photonic waveguides, offering novel functionalities. |
| 2025-03-28 | Convolutional optimization with convex kernel and power lift | Zhipeng Lu et.al. | 2503.22135 | | | Abstract (click to expand)We focus on establishing the foundational paradigm of a novel optimization theory based on convolution with convex kernels. Our goal is to devise a morally deterministic model of locating the global optima of an arbitrary function, which is distinguished from most commonly used statistical models. Limited preliminary numerical results are provided to test the efficiency of some specific algorithms derived from our paradigm, which we hope to stimulate further practical interest. |
| 2025-03-28 | A production planning benchmark for real-world refinery-petrochemical complexes | Wenli Du, Chuan Wang, Chen Fan et.al. | 2503.22057 | link | | Abstract (click to expand)To achieve digital intelligence transformation and carbon neutrality, effective production planning is crucial for integrated refinery-petrochemical complexes. Modern refinery planning relies on advanced optimization techniques, whose development requires reproducible benchmark problems. However, existing benchmarks lack practical context or impose oversimplified assumptions, limiting their applicability to enterprise-wide optimization. To bridge the substantial gap between theoretical research and industrial applications, this paper introduces the first open-source, demand-driven benchmark for industrial-scale refinery-petrochemical complexes with transparent model formulations and comprehensive input parameters. The benchmark incorporates a novel port-stream hybrid superstructure for modular modeling and broad generalizability. Key secondary processing units are represented using the delta-base approach grounded in historical data. Three real-world cases have been constructed to encompass distinct scenario characteristics, respectively addressing (1) a stand-alone refinery without integer variables, (2) chemical site integration with inventory-related integer variables, and (3) multi-period planning. All model parameters are fully accessible. Additionally, this paper provides an analysis of computational performance, ablation experiments on delta-base modeling, and application scenarios for the proposed benchmark. |
| 2025-03-27 | Multimodal Data Integration for Sustainable Indoor Gardening: Tracking Anyplant with Time Series Foundation Model | Seyed Hamidreza Nabaei, Zeyang Zheng, Dong Chen et.al. | 2503.21932 | | Accepted at ASCE International Conference on Computing in Civil Engineering (i3ce) | Abstract (click to expand)Indoor gardening within sustainable buildings offers a transformative solution to urban food security and environmental sustainability. By 2030, urban farming, including Controlled Environment Agriculture (CEA) and vertical farming, is expected to grow at a compound annual growth rate (CAGR) of 13.2% from 2024 to 2030, according to market reports. This growth is fueled by advancements in Internet of Things (IoT) technologies, sustainable innovations such as smart growing systems, and the rising interest in green interior design. This paper presents a novel framework that integrates computer vision, machine learning (ML), and environmental sensing for the automated monitoring of plant health and growth. Unlike previous approaches, this framework combines RGB imagery, plant phenotyping data, and environmental factors such as temperature and humidity, to predict plant water stress in a controlled growth environment. The system utilizes high-resolution cameras to extract phenotypic features, such as RGB, plant area, height, and width while employing the Lag-Llama time series model to analyze and predict water stress. Experimental results demonstrate that integrating RGB, size ratios, and environmental data significantly enhances predictive accuracy, with the Fine-tuned model achieving the lowest errors (MSE = 0.420777, MAE = 0.595428) and reduced uncertainty. These findings highlight the potential of multimodal data and intelligent systems to automate plant care, optimize resource consumption, and align indoor gardening with sustainable building management practices, paving the way for resilient, green urban spaces. |
| 2025-03-27 | Data-Driven Nonlinear Model Reduction to Spectral Submanifolds via Oblique Projection | Leonardo Bettini, Bálint Kaszás, Bernhard Zybach et.al. | 2503.21895 | | | Abstract (click to expand)The dynamics in a primary Spectral Submanifold (SSM) constructed over the slowest modes of a dynamical system provide an ideal reduced-order model for nearby trajectories. Modeling the dynamics of trajectories further away from the primary SSM, however, is difficult if the linear part of the system exhibits strong non-normal behavior. Such non-normality implies that simply projecting trajectories onto SSMs along directions normal to the slow linear modes will not pair those trajectories correctly with their reduced counterparts on the SSMs. In principle, a well-defined nonlinear projection along a stable invariant foliation exists and would exactly match the full dynamics to the SSM-reduced dynamics. This foliation, however, cannot realistically be constructed from practically feasible amounts and distributions of experimental data. Here we develop an oblique projection technique that is able to approximate this foliation efficiently, even from a single experimental trajectory of a significantly non-normal and nonlinear beam. |
| 2025-03-27 | CMADiff: Cross-Modal Aligned Diffusion for Controllable Protein Generation | Changjian Zhou, Yuexi Qiu, Tongtong Ling et.al. | 2503.21450 | | | Abstract (click to expand)AI-assisted protein design has emerged as a critical tool for advancing biotechnology, as deep generative models have demonstrated their reliability in this domain. However, most existing models primarily utilize protein sequence or structural data for training, neglecting the physicochemical properties of proteins.Moreover, they are deficient to control the generation of proteins in intuitive conditions. To address these limitations,we propose CMADiff here, a novel framework that enables controllable protein generation by aligning the physicochemical properties of protein sequences with text-based descriptions through a latent diffusion process. Specifically, CMADiff employs a Conditional Variational Autoencoder (CVAE) to integrate physicochemical features as conditional input, forming a robust latent space that captures biological traits. In this latent space, we apply a conditional diffusion process, which is guided by BioAligner, a contrastive learning-based module that aligns text descriptions with protein features, enabling text-driven control over protein sequence generation. Validated by a series of evaluations including AlphaFold3, the experimental results indicate that CMADiff outperforms protein sequence generation benchmarks and holds strong potential for future applications. The implementation and code are available at https://github.com/HPC-NEAU/PhysChemDiff. |
| 2025-03-27 | Large Language Models for Traffic and Transportation Research: Methodologies, State of the Art, and Future Opportunities | Yimo Yan, Yejia Liao, Guanhao Xu et.al. | 2503.21330 | | | Abstract (click to expand)The rapid rise of Large Language Models (LLMs) is transforming traffic and transportation research, with significant advancements emerging between the years 2023 and 2025 -- a period marked by the inception and swift growth of adopting and adapting LLMs for various traffic and transportation applications. However, despite these significant advancements, a systematic review and synthesis of the existing studies remain lacking. To address this gap, this paper provides a comprehensive review of the methodologies and applications of LLMs in traffic and transportation, highlighting their ability to process unstructured textual data to advance transportation research. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. Our analysis identifies critical research gaps. From the methodological perspective, many research gaps can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, we identify numerous opportunities for LLMs to tackle a variety of traffic and transportation challenges, building upon existing research. By synthesizing these findings, this review not only clarifies the current state of LLM adoption and adaptation in traffic and transportation but also proposes future research directions, paving the way for smarter and more sustainable transportation systems. |
| 2025-03-27 | ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition | Yujie Liu, Zonglin Yang, Tong Xie et.al. | 2503.21248 | | | Abstract (click to expand)Large language models (LLMs) have demonstrated potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. To address this gap, we introduce the first large-scale benchmark for evaluating LLMs with a near-sufficient set of sub-tasks of scientific discovery: inspiration retrieval, hypothesis composition, and hypothesis ranking. We develop an automated framework that extracts critical components - research questions, background surveys, inspirations, and hypotheses - from scientific papers across 12 disciplines, with expert validation confirming its accuracy. To prevent data contamination, we focus exclusively on papers published in 2024, ensuring minimal overlap with LLM pretraining data. Our evaluation reveals that LLMs perform well in retrieving inspirations, an out-of-distribution task, suggesting their ability to surface novel knowledge associations. This positions LLMs as "research hypothesis mines", capable of facilitating automated scientific discovery by generating innovative hypotheses at scale with minimal human intervention. |
| 2025-03-27 | GPU-Accelerated Charge-Equilibration for Shadow Molecular Dynamics in Python | Mehmet Cagri Kaymak, Nicholas Lubbers, Christian F. A. Negre et.al. | 2503.21176 | | | Abstract (click to expand)With recent advancements in machine learning for interatomic potentials, Python has become the go-to programming language for exploring new ideas. While machine-learning potentials are often developed in Python-based frameworks, existing molecular dynamics software is predominantly written in lower-level languages. This disparity complicates the integration of machine learning potentials into these molecular dynamics libraries. Additionally, machine learning potentials typically focus on local features, often neglecting long-range electrostatics due to computational complexities. This is a key limitation as applications can require long-range electrostatics and even flexible charges to achieve the desired accuracy. Recent charge equilibration models can address these issues, but they require iterative solvers to assign relaxed flexible charges to the atoms. Conventional implementations also demand very tight convergence to achieve long-term stability, further increasing computational cost. In this work, we present a scalable Python implementation of a recently proposed shadow molecular dynamics scheme based on a charge equilibration model, which avoids the convergence problem while maintaining long-term energy stability and accuracy of observable properties. To deliver a functional and user-friendly Python-based library, we implemented an efficient neighbor list algorithm, Particle Mesh Ewald, and traditional Ewald summation techniques, leveraging the GPU-accelerated power of Triton and PyTorch. We integrated these approaches with the Python-based shadow molecular dynamics scheme, enabling fast charge equilibration for scalable machine learning potentials involving systems with hundreds of thousands of atoms. |
| 2025-03-26 | FinAudio: A Benchmark for Audio Large Language Models in Financial Applications | Yupeng Cao, Haohang Li, Yangyang Yu et.al. | 2503.20990 | | | Abstract (click to expand)Audio Large Language Models (AudioLLMs) have received widespread attention and have significantly improved performance on audio tasks such as conversation, audio understanding, and automatic speech recognition (ASR). Despite these advancements, there is an absence of a benchmark for assessing AudioLLMs in financial scenarios, where audio data, such as earnings conference calls and CEO speeches, are crucial resources for financial analysis and investment decisions. In this paper, we introduce \textsc{FinAudio}, the first benchmark designed to evaluate the capacity of AudioLLMs in the financial domain. We first define three tasks based on the unique characteristics of the financial domain: 1) ASR for short financial audio, 2) ASR for long financial audio, and 3) summarization of long financial audio. Then, we curate two short and two long audio datasets, respectively, and develop a novel dataset for financial audio summarization, comprising the \textsc{FinAudio} benchmark. Then, we evaluate seven prevalent AudioLLMs on \textsc{FinAudio}. Our evaluation reveals the limitations of existing AudioLLMs in the financial domain and offers insights for improving AudioLLMs. All datasets and codes will be released. |
| 2025-03-26 | TransDiffSBDD: Causality-Aware Multi-Modal Structure-Based Drug Design | Xiuyuan Hu, Guoqing Liu, Can Chen et.al. | 2503.20913 | | | Abstract (click to expand)Structure-based drug design (SBDD) is a critical task in drug discovery, requiring the generation of molecular information across two distinct modalities: discrete molecular graphs and continuous 3D coordinates. However, existing SBDD methods often overlook two key challenges: (1) the multi-modal nature of this task and (2) the causal relationship between these modalities, limiting their plausibility and performance. To address both challenges, we propose TransDiffSBDD, an integrated framework combining autoregressive transformers and diffusion models for SBDD. Specifically, the autoregressive transformer models discrete molecular information, while the diffusion model samples continuous distributions, effectively resolving the first challenge. To address the second challenge, we design a hybrid-modal sequence for protein-ligand complexes that explicitly respects the causality between modalities. Experiments on the CrossDocked2020 benchmark demonstrate that TransDiffSBDD outperforms existing baselines. |
| 2025-03-26 | Technical Note: Continuum Theory of Mixture for Three-phase Thermomechanical Model of Fiber-reinforced Aerogel Composites | Pratyush Kumar Singh, Danial Faghihi et.al. | 2503.20713 | | | Abstract (click to expand)We present a thermodynamically consistent three-phase model for the coupled thermal transport and mechanical deformation of ceramic aerogel porous composite materials, which is formulated via continuum mixture theory. The composite comprises a solid silica skeleton, a gaseous fluid phase, and dispersed solid fibers. The thermal transport model incorporates the effects of meso- and macro-pore size variations due to the Knudsen effect, achieved by upscaling phonon transport relations to derive constitutive equations for the fluid thermal conductivity. The mechanical model captures solid-solid and solid-fluid interactions through momentum exchange between phases. A mixed finite element formulation is employed to solve the multiphase model, and numerical studies are conducted to analyze key features of the computational model. |
| 2025-03-26 | General Method for Conversion Between Multimode Network Parameters | Alexander Zhuravlev, Juan D. Baena et.al. | 2503.20298 | | | Abstract (click to expand)Different types of network parameters have been used in electronics since long ago. The most typical network parameters, but not the only ones, are \(S\), \(T\), \(ABCD\), \(Z\), \(Y\) , and \(h\) that relate input and output signals in different ways. There exist practical formulas for conversion between them. Due to the development of powerful software tools that can deal efficiently and accurately with higher-order modes in each port, researchers need conversion rules between multimode network parameters. However, the usual way to get each conversion rule is just developing cumbersome algebraic manipulations which, at the end, are useful only for some specific conversion. Here, we propose a general algebraic method to obtain any conversion rule between different multimode network parameters. It is based on the assumption of a state vector space and each conversion rule between network parameters can be interpreted as a simple change of basis. This procedure explains any conversion between multimode network parameters under the same algebraic steps. |
| 2025-03-26 | Dynamic Learning and Productivity for Data Analysts: A Bayesian Hidden Markov Model Perspective | Yue Yin et.al. | 2503.20233 | | 29 pages; a shorter 11-page version is accepted by HCI International (HCII) 2025; | Abstract (click to expand)Data analysts are essential in organizations, transforming raw data into insights that drive decision-making and strategy. This study explores how analysts' productivity evolves on a collaborative platform, focusing on two key learning activities: writing queries and viewing peer queries. While traditional research often assumes static models, where performance improves steadily with cumulative learning, such models fail to capture the dynamic nature of real-world learning. To address this, we propose a Hidden Markov Model (HMM) that tracks how analysts transition between distinct learning states based on their participation in these activities. Using an industry dataset with 2,001 analysts and 79,797 queries, this study identifies three learning states: novice, intermediate, and advanced. Productivity increases as analysts advance to higher states, reflecting the cumulative benefits of learning. Writing queries benefits analysts across all states, with the largest gains observed for novices. Viewing peer queries supports novices but may hinder analysts in higher states due to cognitive overload or inefficiencies. Transitions between states are also uneven, with progression from intermediate to advanced being particularly challenging. This study advances understanding of into dynamic learning behavior of knowledge worker and offers practical implications for designing systems, optimizing training, enabling personalized learning, and fostering effective knowledge sharing. |
| 2025-03-26 | Solving 2-D Helmholtz equation in the rectangular, circular, and elliptical domains using neural networks | D. Veerababu, Prasanta K. Ghosh et.al. | 2503.20222 | | 59 pages | Abstract (click to expand)Physics-informed neural networks offered an alternate way to solve several differential equations that govern complicated physics. However, their success in predicting the acoustic field is limited by the vanishing-gradient problem that occurs when solving the Helmholtz equation. In this paper, a formulation is presented that addresses this difficulty. The problem of solving the two-dimensional Helmholtz equation with the prescribed boundary conditions is posed as an unconstrained optimization problem using trial solution method. According to this method, a trial neural network that satisfies the given boundary conditions prior to the training process is constructed using the technique of transfinite interpolation and the theory of R-functions. This ansatz is initially applied to the rectangular domain and later extended to the circular and elliptical domains. The acoustic field predicted from the proposed formulation is compared with that obtained from the two-dimensional finite element methods. Good agreement is observed in all three domains considered. Minor limitations associated with the proposed formulation and their remedies are also discussed. |
| 2025-03-25 | Lossy Compression of Scientific Data: Applications Constrains and Requirements | Franck Cappello, Allison Baker, Ebru Bozda et.al. | 2503.20031 | | 33 pages | Abstract (click to expand)Increasing data volumes from scientific simulations and instruments (supercomputers, accelerators, telescopes) often exceed network, storage, and analysis capabilities. The scientific community's response to this challenge is scientific data reduction. Reduction can take many forms, such as triggering, sampling, filtering, quantization, and dimensionality reduction. This report focuses on a specific technique: lossy compression. Lossy compression retains all data points, leveraging correlations and controlled reduced accuracy. Quality constraints, especially for quantities of interest, are crucial for preserving scientific discoveries. User requirements also include compression ratio and speed. While many papers have been published on lossy compression techniques and reference datasets are shared by the community, there is a lack of detailed specifications of application needs that can guide lossy compression researchers and developers. This report fills this gap by reporting on the requirements and constraints of nine scientific applications covering a large spectrum of domains (climate, combustion, cosmology, fusion, light sources, molecular dynamics, quantum circuit simulation, seismology, and system logs). The report also details key lossy compression technologies (SZ, ZFP, MGARD, LC, SPERR, DCTZ, TEZip, LibPressio), discussing their history, principles, error control, hardware support, features, and impact. By presenting both application needs and compression technologies, the report aims to inspire new research to fill existing gaps. |
| 2025-03-25 | A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM | Sanjeev Kumar, D. Thomas Seidl, Brian N. Granzow et.al. | 2503.19782 | | 44 pages, 15 figures | Abstract (click to expand)Accurate identification of material parameters is crucial for predictive modeling in computational mechanics. The two primary approaches in the experimental mechanics' community for calibration from full-field digital image correlation data are known as finite element model updating (FEMU) and the virtual fields method (VFM). In VFM, the objective function is a squared mismatch between internal and external virtual work or power. In FEMU, the objective function quantifies the weighted mismatch between model predictions and corresponding experimentally measured quantities of interest. It is minimized by iteratively updating the parameters of an FE model. While FEMU is seen as more flexible, VFM is commonly used instead of FEMU due to its considerably greater computational expense. However, comparisons between the two methods usually involve approximations of gradients or sensitivities with finite difference schemes, thereby making direct assessments difficult. Hence, in this study, we rigorously compare VFM and FEMU in the context of numerically-exact sensitivities obtained through local sensitivity analyses and the application of automatic differentiation software. To this end, both methods are tested on a finite strain elastoplasticity model. We conduct a series of test cases to assess both methods' robustness under practical challenges. |
| 2025-03-25 | Decoupled Dynamics Framework with Neural Fields for 3D Spatio-temporal Prediction of Vehicle Collisions | Sanghyuk Kim, Minsik Seo, Namwoo Kang et.al. | 2503.19712 | | 24 pages, 13 figures | Abstract (click to expand)This study proposes a neural framework that predicts 3D vehicle collision dynamics by independently modeling global rigid-body motion and local structural deformation. Unlike approaches directly predicting absolute displacement, this method explicitly separates the vehicle's overall translation and rotation from its structural deformation. Two specialized networks form the core of the framework: a quaternion-based Rigid Net for rigid motion and a coordinate-based Deformation Net for local deformation. By independently handling fundamentally distinct physical phenomena, the proposed architecture achieves accurate predictions without requiring separate supervision for each component. The model, trained on only 10% of available simulation data, significantly outperforms baseline models, including single multi-layer perceptron (MLP) and deep operator networks (DeepONet), with prediction errors reduced by up to 83%. Extensive validation demonstrates strong generalization to collision conditions outside the training range, accurately predicting responses even under severe impacts involving extreme velocities and large impact angles. Furthermore, the framework successfully reconstructs high-resolution deformation details from low-resolution inputs without increased computational effort. Consequently, the proposed approach provides an effective, computationally efficient method for rapid and reliable assessment of vehicle safety across complex collision scenarios, substantially reducing the required simulation data and time while preserving prediction fidelity. |
| 2025-03-25 | Characteristic boundary conditions for Hybridizable Discontinuous Galerkin methods | Jan Ellmenreich, Matteo Giacomini, Antonio Huerta et.al. | 2503.19684 | | | Abstract (click to expand)In this work we introduce the concept of characteristic boundary conditions (CBCs) within the framework of Hybridizable Discontinuous Galerkin (HDG) methods, including both the Navier-Stokes characteristic boundary conditions (NSCBCs) and a novel approach to generalized characteristic relaxation boundary conditions (GRCBCs). CBCs are based on the characteristic decomposition of the compressible Euler equations and are designed to prevent the reflection of waves at the domain boundaries. We show the effectiveness of the proposed method for weakly compressible flows through a series of numerical experiments by comparing the results with common boundary conditions in the HDG setting and reference solutions available in the literature. In particular, HDG with CBCs show superior performance minimizing the reflection of vortices at artificial boundaries, for both inviscid and viscous flows. |
| 2025-03-25 | Estimation of the Acoustic Field in a Uniform Duct with Mean Flow using Neural Networks | D. Veerababu, Prasanta K. Ghosh et.al. | 2503.19412 | | 23 pages | Abstract (click to expand)The study of sound propagation in a uniform duct having a mean flow has many applications, such as in the design of gas turbines, heating, ventilation and air conditioning ducts, automotive intake and exhaust systems, and in the modeling of speech. In this paper, the convective effects of the mean flow on the plane wave acoustic field inside a uniform duct were studied using artificial neural networks. The governing differential equation and the associated boundary conditions form a constrained optimization problem. It is converted to an unconstrained optimization problem and solved by approximating the acoustic field variable to a neural network. The complex-valued acoustic pressure and particle velocity were predicted at different frequencies, and validated against the analytical solution and the finite element models. The effect of the mean flow is studied in terms of the acoustic impedance. A closed-form expression that describes the influence of various factors on the acoustic field is derived. |
| 2025-03-24 | Multi-Physics Inverse Design of Varifocal Optical Devices using Data-Driven Surrogates and Differential Modeling | Zeqing Jin, Zhaocheng Liu, Nagi Elabbasi et.al. | 2503.18911 | | 15 pages, 4 figures | Abstract (click to expand)Designing a new varifocal architecture in AR glasses poses significant challenges due to the complex interplay of multiple physics disciplines, including innovated piezo-electric material, solid mechanics, electrostatics, and optics. Traditional design methods, which treat each physics separately, are insufficient for this problem as they fail to establish the intricate relationships among design parameters in such a large and sensitive space, leading to suboptimal solutions. To address this challenge, we propose a novel design pipeline, mPhDBBs (multi-Physics Differential Building Blocks), that integrates these diverse physics through a graph neural network-based surrogate model and a differentiable ray tracing model. A hybrid optimization method combining evolutionary and gradient approaches is employed to efficiently determine superior design variables that achieve desired optical objectives, such as focal length and focusing quality. Our results demonstrate the effectiveness of mPhDBBs, achieving high accuracy with minimal training data and computational resources, resulting in a speedup of at least 1000 times compared to non-gradient-based methods. This work offers a promising paradigm shift in product design, enabling rapid and accurate optimization of complex multi-physics systems, and demonstrates its adaptability to other inverse design problems. |
| 2025-03-24 | Differentiable Simulator for Electrically Reconfigurable Electromagnetic Structures | Johannes Müller, Dennis Philipp, Matthias Günther et.al. | 2503.18479 | | | Abstract (click to expand)This paper introduces a novel CUDA-enabled PyTorch-based framework designed for the gradient-based optimization of such reconfigurable electromagnetic structures with electrically tunable parameters. Traditional optimization techniques for these structures often rely on non-gradient-based methods, limiting efficiency and flexibility. Our framework leverages automatic differentiation, facilitating the application of gradient-based optimization methods. This approach is particularly advantageous for embedding within deep learning frameworks, enabling sophisticated optimization strategies. We demonstrate the framework's effectiveness through comprehensive simulations involving resonant structures with tunable parameters. Key contributions include the efficient solution of the inverse problem. The framework's performance is validated using three different resonant structures: a single-loop copper wire (Unit-Cell) as well as an 8x1 and an 8x8 array of resonant unit cells with multiple inductively coupled unit cells (1d and 2d Metasurfaces). Results show precise in-silico control over the magnetic field's component normal to the surface of each resonant structure, achieving desired field strengths with minimal error. The proposed framework is compatible with existing simulation software. This PyTorch-based framework sets the stage for advanced electromagnetic control strategies for resonant structures with application in e.g. MRI, providing a robust platform for further exploration and innovation in the design and optimization of resonant electromagnetic structures. |
| 2025-03-24 | DeepFund: Will LLM be Professional at Fund Investment? A Live Arena Perspective | Changlun Li, Yao Shi, Yuyu Luo et.al. | 2503.18313 | | Work in progress | Abstract (click to expand)Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but their effectiveness in financial decision making, particularly in fund investment, remains inadequately evaluated. Current benchmarks primarily assess LLMs understanding of financial documents rather than their ability to manage assets or analyze trading opportunities in dynamic market conditions. A critical limitation in existing evaluation methodologies is the backtesting approach, which suffers from information leakage when LLMs are evaluated on historical data they may have encountered during pretraining. This paper introduces DeepFund, a comprehensive platform for evaluating LLM based trading strategies in a simulated live environment. Our approach implements a multi agent framework where LLMs serve as both analysts and managers, creating a realistic simulation of investment decision making. The platform employs a forward testing methodology that mitigates information leakage by evaluating models on market data released after their training cutoff dates. We provide a web interface that visualizes model performance across different market conditions and investment parameters, enabling detailed comparative analysis. Through DeepFund, we aim to provide a more accurate and fair assessment of LLMs capabilities in fund investment, offering insights into their potential real world applications in financial markets. |
| 2025-03-23 | The Power of Small LLMs in Geometry Generation for Physical Simulations | Ossama Shafiq, Bahman Ghiassi, Alessio Alexiadis et.al. | 2503.18178 | | 24 pages, 17 figures | Abstract (click to expand)Engineers widely rely on simulation platforms like COMSOL or ANSYS to model and optimise processes. However, setting up such simulations requires expertise in defining geometry, generating meshes, establishing boundary conditions, and configuring solvers. This research aims to simplify this process by enabling engineers to describe their setup in plain language, allowing a Large Language Model (LLM) to generate the necessary input files for their specific application. This novel approach allows establishing a direct link between natural language and complex engineering tasks. Building on previous work that evaluated various LLMs for generating input files across simple and complex geometries, this study demonstrates that small LLMs - specifically, Phi-3 Mini and Qwen-2.5 1.5B - can be fine-tuned to generate precise engineering geometries in GMSH format. Through Low-Rank Adaptation (LoRA), we curated a dataset of 480 instruction-output pairs encompassing simple shapes (squares, rectangles, circles, and half circles) and more complex structures (I-beams, cylindrical pipes, and bent pipes). The fine-tuned models produced high-fidelity outputs, handling routine geometry generation with minimal intervention. While challenges remain with geometries involving combinations of multiple bodies, this study demonstrates that fine-tuned small models can outperform larger models like GPT-4o in specialised tasks, offering a precise and resource-efficient alternative for engineering applications. |
| 2025-03-23 | Strategic Prompt Pricing for AIGC Services: A User-Centric Approach | Xiang Li, Bing Luo, Jianwei Huang et.al. | 2503.18168 | | accepted in WiOpt 2025 | Abstract (click to expand)The rapid growth of AI-generated content (AIGC) services has created an urgent need for effective prompt pricing strategies, yet current approaches overlook users' strategic two-step decision-making process in selecting and utilizing generative AI models. This oversight creates two key technical challenges: quantifying the relationship between user prompt capabilities and generation outcomes, and optimizing platform payoff while accounting for heterogeneous user behaviors. We address these challenges by introducing prompt ambiguity, a theoretical framework that captures users' varying abilities in prompt engineering, and developing an Optimal Prompt Pricing (OPP) algorithm. Our analysis reveals a counterintuitive insight: users with higher prompt ambiguity (i.e., lower capability) exhibit non-monotonic prompt usage patterns, first increasing then decreasing with ambiguity levels, reflecting complex changes in marginal utility. Experimental evaluation using a character-level GPT-like model demonstrates that our OPP algorithm achieves up to 31.72% improvement in platform payoff compared to existing pricing mechanisms, validating the importance of user-centric prompt pricing in AIGC services. |
| 2025-03-23 | (G)I-DLE: Generative Inference via Distribution-preserving Logit Exclusion with KL Divergence Minimization for Constrained Decoding | Hanwool Lee et.al. | 2503.18050 | | preprint | Abstract (click to expand)We propose (G)I-DLE, a new approach to constrained decoding that leverages KL divergence minimization to preserve the intrinsic conditional probability distribution of autoregressive language models while excluding undesirable tokens. Unlike conventional methods that naively set banned tokens' logits to \(-\infty\) , which can distort the conversion from raw logits to posterior probabilities and increase output variance, (G)I-DLE re-normalizes the allowed token probabilities to minimize such distortion. We validate our method on the K2-Eval dataset, specifically designed to assess Korean language fluency, logical reasoning, and cultural appropriateness. Experimental results on Qwen2.5 models (ranging from 1.5B to 14B) demonstrate that G-IDLE not only boosts mean evaluation scores but also substantially reduces the variance of output quality. |
| 2025-03-23 | Financial Wind Tunnel: A Retrieval-Augmented Market Simulator | Bokai Cao, Xueyuan Lin, Yiyan Qi et.al. | 2503.17909 | | | Abstract (click to expand)Market simulator tries to create high-quality synthetic financial data that mimics real-world market dynamics, which is crucial for model development and robust assessment. Despite continuous advancements in simulation methodologies, market fluctuations vary in terms of scale and sources, but existing frameworks often excel in only specific tasks. To address this challenge, we propose Financial Wind Tunnel (FWT), a retrieval-augmented market simulator designed to generate controllable, reasonable, and adaptable market dynamics for model testing. FWT offers a more comprehensive and systematic generative capability across different data frequencies. By leveraging a retrieval method to discover cross-sectional information as the augmented condition, our diffusion-based simulator seamlessly integrates both macro- and micro-level market patterns. Furthermore, our framework allows the simulation to be controlled with wide applicability, including causal generation through "what-if" prompts or unprecedented cross-market trend synthesis. Additionally, we develop an automated optimizer for downstream quantitative models, using stress testing of simulated scenarios via FWT to enhance returns while controlling risks. Experimental results demonstrate that our approach enables the generalizable and reliable market simulation, significantly improve the performance and adaptability of downstream models, particularly in highly complex and volatile market conditions. Our code and data sample is available at https://anonymous.4open.science/r/fwt_-E852 |
| 2025-03-22 | Accelerating and enhancing thermodynamic simulations of electrochemical interfaces | Xiaochen Du, Mengren Liu, Jiayu Peng et.al. | 2503.17870 | link | 19 pages main text, 5 figures, supplementary information (SI) in ancillary files | Abstract (click to expand)Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsorbates, and electrolyte. Predicting stable surface structures remains challenging, as traditional surface Pourbaix diagrams tend to either rely on expert knowledge or costly \(\textit{ab initio}\) sampling, and neglect thermodynamic equilibration with the environment. Machine learning (ML) potentials can accelerate static modeling but often overlook dynamic surface transformations. Here, we extend the Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) method to autonomously sample surface reconstructions modeled under aqueous electrochemical conditions. Through fine-tuning foundational ML force fields, we accurately and efficiently predict surface energetics, recovering known Pt(111) phases and revealing new LaMnO\(_\mathrm{3}\) (001) surface reconstructions. By explicitly accounting for bulk-electrolyte equilibria, our framework enhances electrochemical stability predictions, offering a scalable approach to understanding and designing materials for electrochemical applications. |
| 2025-03-22 | Generalized Scattering Matrix Synthesis for Hybrid Systems with Multiple Scatterers and Antennas Using Independent Structure Simulations | Chenbo Shi, Shichen Liang, Jin Pan et.al. | 2503.17616 | | | Abstract (click to expand)This paper presents a unified formulation for calculating the generalized scattering matrix (GS-matrix) of hybrid systems involving multiple scatterers and antennas. The GS-matrix of the entire system is synthesized through the scattering matrices and GS-matrices of each independent component, using the addition theorem of vector spherical wavefunctions and fully matrix-based operations. Since our formulation is applicable to general antenna-scatterer hybrid systems, previous formulas for multiple scattering and antenna arrays become special cases of our approach. This also establishes our formulation as a universal domain decomposition method for analyzing the electromagnetic performance of hybrid systems. We provide numerous numerical examples to comprehensively demonstrate the capabilities and compatibility of the proposed formulation, including its potential application in studying the effects of structural rotation. |
| 2025-03-21 | Adjoint Sensitivities for the Optimization of Nonlinear Structural Dynamics via Spectral Submanifolds | Matteo Pozzi, Jacopo Marconi, Shobhit Jain et.al. | 2503.17431 | | | Abstract (click to expand)This work presents an optimization framework for tailoring the nonlinear dynamic response of lightly damped mechanical systems using Spectral Submanifold (SSM) reduction. We derive the SSM-based backbone curve and its sensitivity with respect to parameters up to arbitrary polynomial orders, enabling efficient and accurate optimization of the nonlinear frequency-amplitude relation. We use the adjoint method to derive sensitivity expressions, which drastically reduces the computational cost compared to direct differentiation as the number of parameters increases. An important feature of this framework is the automatic adjustment of the expansion order of SSM-based ROMs using user-defined error tolerances during the optimization process. We demonstrate the effectiveness of the approach in optimizing the nonlinear response over several numerical examples of mechanical systems. Hence, the proposed framework extends the applicability of SSM-based optimization methods to practical engineering problems, offering a robust tool for the design and optimization of nonlinear mechanical structures. |
| 2025-03-20 | Accelerated Medicines Development using a Digital Formulator and a Self-Driving Tableting DataFactory | Faisal Abbas, Mohammad Salehian, Peter Hou et.al. | 2503.17411 | link | | Abstract (click to expand)Pharmaceutical tablet formulation and process development, traditionally a complex and multi-dimensional decision-making process, necessitates extensive experimentation and resources, often resulting in suboptimal solutions. This study presents an integrated platform for tablet formulation and manufacturing, built around a Digital Formulator and a Self-Driving Tableting DataFactory. By combining predictive modelling, optimisation algorithms, and automation, this system offers a material-to-product approach to predict and optimise critical quality attributes for different formulations, linking raw material attributes to key blend and tablet properties, such as flowability, porosity, and tensile strength. The platform leverages the Digital Formulator, an in-silico optimisation framework that employs a hybrid system of models - melding data-driven and mechanistic models - to identify optimal formulation settings for manufacturability. Optimised formulations then proceed through the self-driving Tableting DataFactory, which includes automated powder dosing, tablet compression and performance testing, followed by iterative refinement of process parameters through Bayesian optimisation methods. This approach accelerates the timeline from material characterisation to development of an in-specification tablet within 6 hours, utilising less than 5 grams of API, and manufacturing small batch sizes of up to 1,440 tablets with augmented and mixed reality enabled real-time quality control within 24 hours. Validation across multiple APIs and drug loadings underscores the platform's capacity to reliably meet target quality attributes, positioning it as a transformative solution for accelerated and resource-efficient pharmaceutical development. |
| 2025-03-21 | ML-Based Bidding Price Prediction for Pay-As-Bid Ancillary Services Markets: A Use Case in the German Control Reserve Market | Vincent Bezold, Lukas Baur, Alexander Sauer et.al. | 2503.17214 | | | Abstract (click to expand)The increasing integration of renewable energy sources has led to greater volatility and unpredictability in electricity generation, posing challenges to grid stability. Ancillary service markets, such as the German control reserve market, allow industrial consumers and producers to offer flexibility in their power consumption or generation, contributing to grid stability while earning additional income. However, many participants use simple bidding strategies that may not maximize their revenues. This paper presents a methodology for forecasting bidding prices in pay-as-bid ancillary service markets, focusing on the German control reserve market. We evaluate various machine learning models, including Support Vector Regression, Decision Trees, and k-Nearest Neighbors, and compare their performance against benchmark models. To address the asymmetry in the revenue function of pay-as-bid markets, we introduce an offset adjustment technique that enhances the practical applicability of the forecasting models. Our analysis demonstrates that the proposed approach improves potential revenues by 27.43 % to 37.31 % compared to baseline models. When analyzing the relationship between the model forecasting errors and the revenue, a negative correlation is measured for three markets; according to the results, a reduction of 1 EUR/MW model price forecasting error (MAE) statistically leads to a yearly revenue increase between 483 EUR/MW and 3,631 EUR/MW. The proposed methodology enables industrial participants to optimize their bidding strategies, leading to increased earnings and contributing to the efficiency and stability of the electrical grid. |
| 2025-03-21 | A Comprehensive Framework for Predictive Computational Modeling of Growth and Remodeling in Tissue-Engineered Cardiovascular Implants | Mahmoud Sesa, Hagen Holthusen, Christian Böhm et.al. | 2503.17151 | | Preprint submitted to Springer Nature | Abstract (click to expand)Developing clinically viable tissue-engineered cardiovascular implants remains a formidable challenge. Achieving reliable and durable outcomes requires a deeper understanding of the fundamental mechanisms driving tissue evolution during in vitro maturation. Although considerable progress has been made in modeling soft tissue growth and remodeling, studies focused on the early stages of tissue engineering remain limited. Here, we present a general, thermodynamically consistent model to predict tissue evolution and mechanical response throughout maturation. The formulation utilizes a stress-driven homeostatic surface to capture volumetric growth, coupled with an energy-based approach to describe collagen densification via the strain energy of the fibers. We further employ a co-rotated intermediate configuration to ensure the model's consistency and generality. The framework is demonstrated with two numerical examples: a uniaxially constrained tissue strip validated against experimental data, and a biaxially constrained specimen subjected to a perturbation load. These results highlight the potential of the proposed model to advance the design and optimization of tissue-engineered implants with clinically relevant performance. |
| 2025-03-26 | Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks | Julian Junyan Wang, Victor Xiaoqi Wang et.al. | 2503.16974 | | 97 pages, 20 tables, 15 figures | Abstract (click to expand)This study provides the first comprehensive assessment of consistency and reproducibility in Large Language Model (LLM) outputs in finance and accounting research. We evaluate how consistently LLMs produce outputs given identical inputs through extensive experimentation with 50 independent runs across five common tasks: classification, sentiment analysis, summarization, text generation, and prediction. Using three OpenAI models (GPT-3.5-turbo, GPT-4o-mini, and GPT-4o), we generate over 3.4 million outputs from diverse financial source texts and data, covering MD&As, FOMC statements, finance news articles, earnings call transcripts, and financial statements. Our findings reveal substantial but task-dependent consistency, with binary classification and sentiment analysis achieving near-perfect reproducibility, while complex tasks show greater variability. More advanced models do not consistently demonstrate better consistency and reproducibility, with task-specific patterns emerging. LLMs significantly outperform expert human annotators in consistency and maintain high agreement even where human experts significantly disagree. We further find that simple aggregation strategies across 3-5 runs dramatically improve consistency. We also find that aggregation may come with an additional benefit of improved accuracy for sentiment analysis when using newer models. Simulation analysis reveals that despite measurable inconsistency in LLM outputs, downstream statistical inferences remain remarkably robust. These findings address concerns about what we term "G-hacking," the selective reporting of favorable outcomes from multiple Generative AI runs, by demonstrating that such risks are relatively low for finance and accounting tasks. |
| 2025-03-19 | Reliable Radiologic Skeletal Muscle Area Assessment -- A Biomarker for Cancer Cachexia Diagnosis | Sabeen Ahmed, Nathan Parker, Margaret Park et.al. | 2503.16556 | | 47 pages, 19 figures, 9 Tables | Abstract (click to expand)Cancer cachexia is a common metabolic disorder characterized by severe muscle atrophy which is associated with poor prognosis and quality of life. Monitoring skeletal muscle area (SMA) longitudinally through computed tomography (CT) scans, an imaging modality routinely acquired in cancer care, is an effective way to identify and track this condition. However, existing tools often lack full automation and exhibit inconsistent accuracy, limiting their potential for integration into clinical workflows. To address these challenges, we developed SMAART-AI (Skeletal Muscle Assessment-Automated and Reliable Tool-based on AI), an end-to-end automated pipeline powered by deep learning models (nnU-Net 2D) trained on mid-third lumbar level CT images with 5-fold cross-validation, ensuring generalizability and robustness. SMAART-AI incorporates an uncertainty-based mechanism to flag high-error SMA predictions for expert review, enhancing reliability. We combined the SMA, skeletal muscle index, BMI, and clinical data to train a multi-layer perceptron (MLP) model designed to predict cachexia at the time of cancer diagnosis. Tested on the gastroesophageal cancer dataset, SMAART-AI achieved a Dice score of 97.80% +/- 0.93%, with SMA estimated across all four datasets in this study at a median absolute error of 2.48% compared to manual annotations with SliceOmatic. Uncertainty metrics-variance, entropy, and coefficient of variation-strongly correlated with SMA prediction errors (0.83, 0.76, and 0.73 respectively). The MLP model predicts cachexia with 79% precision, providing clinicians with a reliable tool for early diagnosis and intervention. By combining automation, accuracy, and uncertainty awareness, SMAART-AI bridges the gap between research and clinical application, offering a transformative approach to managing cancer cachexia. |
| 2025-03-20 | Deep Feynman-Kac Methods for High-dimensional Semilinear Parabolic Equations: Revisit | Xiaotao Zheng, Xingye Yue, Jiyang Shi et.al. | 2503.16407 | | | Abstract (click to expand)Deep Feynman-Kac method was first introduced to solve parabolic partial differential equations(PDE) by Beck et al. (SISC, V.43, 2021), named Deep Splitting method since they trained the Neural Networks step by step in the time direction. In this paper, we propose a new training approach with two different features. Firstly, neural networks are trained at all time steps globally, instead of step by step. Secondly, the training data are generated in a new way, in which the method is consistent with a direct Monte Carlo scheme when dealing with a linear parabolic PDE. Numerical examples show that our method has significant improvement both in efficiency and accuracy. |
| 2025-03-20 | Filters reveal emergent structure in computational morphogenesis | Hazhir Aliahmadi, Aidan Sheedy, Greg van Anders et.al. | 2503.16211 | | 17 pages, 9 figures | Abstract (click to expand)Revolutionary advances in both manufacturing and computational morphogenesis raise critical questions about design sensitivity. Sensitivity questions are especially critical in contexts, such as topology optimization, that yield structures with emergent morphology. However, analyzing emergent structures via conventional, perturbative techniques can mask larger-scale vulnerabilities that could manifest in essential components. Risks that fail to appear in perturbative sensitivity analyses will only continue to proliferate as topology optimization-driven manufacturing penetrates more deeply into engineering design and consumer products. Here, we introduce Laplace-transform based computational filters that supplement computational morphogenesis with a set of nonperturbative sensitivity analyses. We demonstrate how this approach identifies important elements of a structure even in the absence of knowledge of the ultimate, optimal structure itself. We leverage techniques from molecular dynamics and implement these methods in open-source codes, demonstrating their application to compliance minimization problems in both 2D and 3D. Our implementation extends straightforwardly to topology optimization for other problems and benefits from the strong scaling properties observed in conventional molecular simulation. |
| 2025-03-20 | Sustainable Open-Data Management for Field Research: A Cloud-Based Approach in the Underlandscape Project | Augusto Ciuffoletti, Letizia Chiti et.al. | 2503.16042 | | 8 pages, 4 figures | Abstract (click to expand)Field-based research projects require a robust suite of ICT services to support data acquisition, documentation, storage, and dissemination. A key challenge lies in ensuring the sustainability of data management - not only during the project's funded period but also beyond its conclusion, when maintenance and support often depend on voluntary efforts. In the Underlandscape project, we tackled this challenge by extensively leveraging public cloud services while minimizing reliance on complex custom infrastructure. This paper provides a comprehensive overview of the project's final infrastructure, detailing the adopted data formats, the cloud-based solutions enabling data management, and the custom applications developed for system integration. |
| 2025-03-20 | Practical Portfolio Optimization with Metaheuristics:Pre-assignment Constraint and Margin Trading | Hang Kin Poon et.al. | 2503.15965 | | | Abstract (click to expand)Portfolio optimization is a critical area in finance, aiming to maximize returns while minimizing risk. Metaheuristic algorithms were shown to solve complex optimization problems efficiently, with Genetic Algorithms and Particle Swarm Optimization being among the most popular methods. This paper introduces an innovative approach to portfolio optimization that incorporates pre-assignment to limit the search space for investor preferences and better results. Additionally, taking margin trading strategies in account and using a rare performance ratio to evaluate portfolio efficiency. Through an illustrative example, this paper demonstrates that the metaheuristic-based methodology yields superior risk-adjusted returns compared to traditional benchmarks. The results highlight the potential of metaheuristics with help of assets filtering in enhancing portfolio performance in terms of risk adjusted return. |
| 2025-03-20 | WeirdFlows: Anomaly Detection in Financial Transaction Flows | Arthur Capozzi, Salvatore Vilella, Dario Moncalvo et.al. | 2503.15896 | | 12 pages, 6 figures, ITADATA2024 | Abstract (click to expand)In recent years, the digitization and automation of anti-financial crime (AFC) investigative processes have faced significant challenges, particularly the need for interpretability of AI model results and the lack of labeled data for training. Network analysis has emerged as a valuable approach in this context. In this paper, we present WeirdFlows, a top-down search pipeline for detecting potentially fraudulent transactions and non-compliant agents. In a transaction network, fraud attempts are often based on complex transaction patterns that change over time to avoid detection. The WeirdFlows pipeline requires neither an a priori set of patterns nor a training set. In addition, by providing elements to explain the anomalies found, it facilitates and supports the work of an AFC analyst. We evaluate WeirdFlows on a dataset from Intesa Sanpaolo (ISP) bank, comprising 80 million cross-country transactions over 15 months, benchmarking our implementation of the algorithm. The results, corroborated by ISP AFC experts, highlight its effectiveness in identifying suspicious transactions and actors, particularly in the context of the economic sanctions imposed in the EU after February 2022. This demonstrates \textit{WeirdFlows}' capability to handle large datasets, detect complex transaction patterns, and provide the necessary interpretability for formal AFC investigations. |
| 2025-03-19 | Impact of pH and chloride content on the biodegradation of magnesium alloys for medical implants: An in vitro and phase-field study | S. Kovacevic, W. Ali, T. K. Mandal et.al. | 2503.15700 | | | Abstract (click to expand)The individual contributions of pH and chloride concentration to the corrosion kinetics of bioabsorbable magnesium (Mg) alloys remain unresolved despite their significant roles as driving factors in Mg corrosion. This study demonstrates and quantifies hitherto unknown separate effects of pH and chloride content on the corrosion of Mg alloys pertinent to biomedical implant applications. The experimental setup designed for this purpose enables the quantification of the dependence of corrosion on pH and chloride concentration. The in vitro tests conclusively demonstrate that variations in chloride concentration, relevant to biomedical applications, have a negligible effect on corrosion kinetics. The findings identify pH as a critical factor in the corrosion of bioabsorbable Mg alloys. A variationally consistent phase-field model is developed for assessing the degradation of Mg alloys in biological fluids. The model accurately predicts the corrosion performance of Mg alloys observed during the experiments, including their dependence on pH and chloride concentration. The capability of the framework to account for mechano-chemical effects during corrosion is demonstrated in practical orthopaedic applications considering bioabsorbable Mg alloy implants for bone fracture fixation and porous scaffolds for bone tissue engineering. The strategy has the potential to assess the in vitro and in vivo service life of bioabsorbable Mg-based biomedical devices. |
| 2025-03-19 | Shap-MeD | Nicolás Laverde, Melissa Robles, Johan Rodríguez et.al. | 2503.15562 | | | Abstract (click to expand)We present Shap-MeD, a text-to-3D object generative model specialized in the biomedical domain. The objective of this study is to develop an assistant that facilitates the 3D modeling of medical objects, thereby reducing development time. 3D modeling in medicine has various applications, including surgical procedure simulation and planning, the design of personalized prosthetic implants, medical education, the creation of anatomical models, and the development of research prototypes. To achieve this, we leverage Shap-e, an open-source text-to-3D generative model developed by OpenAI, and fine-tune it using a dataset of biomedical objects. Our model achieved a mean squared error (MSE) of 0.089 in latent generation on the evaluation set, compared to Shap-e's MSE of 0.147. Additionally, we conducted a qualitative evaluation, comparing our model with others in the generation of biomedical objects. Our results indicate that Shap-MeD demonstrates higher structural accuracy in biomedical object generation. |
| 2025-03-19 | Design for Sensing and Digitalisation (DSD): A Modern Approach to Engineering Design | Daniel N. Wilke et.al. | 2503.14851 | | 4 pages, conference, SACAM 2025 | Abstract (click to expand)This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the earliest stages of the design process. Unlike traditional methodologies that treat sensing as an afterthought, DSD emphasises sensor integration, signal path optimisation, and real-time data utilisation as core design principles. The paper outlines DSD's key principles, discusses its role in enabling digital twin technology, and argues for its importance in modern engineering education. By adopting DSD, engineers can create more intelligent and adaptable systems that leverage real-time data for continuous design iteration, operational optimisation and data-driven predictive maintenance. |
| 2025-03-18 | Teaching Artificial Intelligence to Perform Rapid, Resolution-Invariant Grain Growth Modeling via Fourier Neural Operator | Iman Peivaste, Ahmed Makradi, Salim Belouettar et.al. | 2503.14568 | link | | Abstract (click to expand)Microstructural evolution, particularly grain growth, plays a critical role in shaping the physical, optical, and electronic properties of materials. Traditional phase-field modeling accurately simulates these phenomena but is computationally intensive, especially for large systems and fine spatial resolutions. While machine learning approaches have been employed to accelerate simulations, they often struggle with resolution dependence and generalization across different grain scales. This study introduces a novel approach utilizing Fourier Neural Operator (FNO) to achieve resolution-invariant modeling of microstructure evolution in multi-grain systems. FNO operates in the Fourier space and can inherently handle varying resolutions by learning mappings between function spaces. By integrating FNO with the phase field method, we developed a surrogate model that significantly reduces computational costs while maintaining high accuracy across different spatial scales. We generated a comprehensive dataset from phase-field simulations using the Fan Chen model, capturing grain evolution over time. Data preparation involved creating input-output pairs with a time shift, allowing the model to predict future microstructures based on current and past states. The FNO-based neural network was trained using sequences of microstructures and demonstrated remarkable accuracy in predicting long-term evolution, even for unseen configurations and higher-resolution grids not encountered during training. |
| 2025-03-17 | AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients | Agnieszka Sroka-Oleksiak, Adam Pardyl, Dawid Rymarczyk et.al. | 2503.14542 | | | Abstract (click to expand)Sepsis is a life-threatening condition which requires rapid diagnosis and treatment. Traditional microbiological methods are time-consuming and expensive. In response to these challenges, deep learning algorithms were developed to identify 14 bacteria species and 3 yeast-like fungi from microscopic images of Gram-stained smears of positive blood samples from sepsis patients. A total of 16,637 Gram-stained microscopic images were used in the study. The analysis used the Cellpose 3 model for segmentation and Attention-based Deep Multiple Instance Learning for classification. Our model achieved an accuracy of 77.15% for bacteria and 71.39% for fungi, with ROC AUC of 0.97 and 0.88, respectively. The highest values, reaching up to 96.2%, were obtained for Cutibacterium acnes, Enterococcus faecium, Stenotrophomonas maltophilia and Nakaseomyces glabratus. Classification difficulties were observed in closely related species, such as Staphylococcus hominis and Staphylococcus haemolyticus, due to morphological similarity, and within Candida albicans due to high morphotic diversity. The study confirms the potential of our model for microbial classification, but it also indicates the need for further optimisation and expansion of the training data set. In the future, this technology could support microbial diagnosis, reducing diagnostic time and improving the effectiveness of sepsis treatment due to its simplicity and accessibility. Part of the results presented in this publication was covered by a patent application at the European Patent Office EP24461637.1 "A computer implemented method for identifying a microorganism in a blood and a data processing system therefor". |
| 2025-03-18 | Tensor-decomposition-based A Priori Surrogate (TAPS) modeling for ultra large-scale simulations | Jiachen Guo, Gino Domel, Chanwook Park et.al. | 2503.13933 | | | Abstract (click to expand)A data-free, predictive scientific AI model, Tensor-decomposition-based A Priori Surrogate (TAPS), is proposed for tackling ultra large-scale engineering simulations with significant speedup, memory savings, and storage gain. TAPS can effectively obtain surrogate models for high-dimensional parametric problems with equivalent zetta-scale ( \(10^{21}\)) degrees of freedom (DoFs). TAPS achieves this by directly obtaining reduced-order models through solving governing equations with multiple independent variables such as spatial coordinates, parameters, and time. The paper first introduces an AI-enhanced finite element-type interpolation function called convolution hierarchical deep-learning neural network (C-HiDeNN) with tensor decomposition (TD). Subsequently, the generalized space-parameter-time Galerkin weak form and the corresponding matrix form are derived. Through the choice of TAPS hyperparameters, an arbitrary convergence rate can be achieved. To show the capabilities of this framework, TAPS is then used to simulate a large-scale additive manufacturing process as an example and achieves around 1,370x speedup, 14.8x memory savings, and 955x storage gain compared to the finite difference method with \(3.46\) billion spatial degrees of freedom (DoFs). As a result, the TAPS framework opens a new avenue for many challenging ultra large-scale engineering problems, such as additive manufacturing and integrated circuit design, among others. |
| 2025-03-17 | Quantum Dynamics Simulation of the Advection-Diffusion Equation | Hirad Alipanah, Feng Zhang, Yongxin Yao et.al. | 2503.13729 | | | Abstract (click to expand)The advection-diffusion equation is simulated on a superconducting quantum computer via several quantum algorithms. Three formulations are considered: (1) Trotterization, (2) variational quantum time evolution (VarQTE), and (3) adaptive variational quantum dynamics simulation (AVQDS). These schemes were originally developed for the Hamiltonian simulation of many-body quantum systems. The finite-difference discretized operator of the transport equation is formulated as a Hamiltonian and solved without the need for ancillary qubits. Computations are conducted on a quantum simulator (IBM Qiskit Aer) and an actual quantum hardware (IBM Fez). The former emulates the latter without the noise. The predicted results are compared with direct numerical simulation (DNS) data with infidelities of the order \(10^{-5}\) . In the quantum simulator, Trotterization is observed to have the lowest infidelity and is suitable for fault-tolerant computation. The AVQDS algorithm requires the lowest gate count and the lowest circuit depth. The VarQTE algorithm is the next best in terms of gate counts, but the number of its optimization variables is directly proportional to the number of qubits. Due to current hardware limitations, Trotterization cannot be implemented, as it has an overwhelming large number of operations. Meanwhile, AVQDS and VarQTE can be executed, but suffer from large errors due to significant hardware noise. These algorithms present a new paradigm for computational transport phenomena on quantum computers. |
| 2025-03-17 | Competitive algorithms for calculating the ground state properties of Bose-Fermi mixtures | Tomasz Świsłocki, Krzysztof Gawryluk, Mirosław Brewczyk et.al. | 2503.13717 | | | Abstract (click to expand)In this work we define, analyze, and compare different numerical schemes that can be used to study the ground state properties of Bose-Fermi systems, such as mixtures of different atomic species under external forces or self-bound quantum droplets. The bosonic atoms are assumed to be condensed and are described by the generalized Gross-Pitaevskii equation. The fermionic atoms, on the other hand, are treated individually, and each atom is associated with a wave function whose evolution follows the Hartree-Fock equation. We solve such a formulated set of equations using a variety of methods, including those based on adiabatic switching of interactions and the imaginary time propagation technique combined with the Gram-Schmidt orthonormalization or the diagonalization of the Hamiltonian matrix. We show how different algorithms compete at the numerical level by studying the mixture in the range of parameters covering the formation of self-bound quantum Bose-Fermi droplets. |
| 2025-03-17 | PERC: a suite of software tools for the curation of cryoEM data with application to simulation, modelling and machine learning | Beatriz Costa-Gomes, Joel Greer, Nikolai Juraschko et.al. | 2503.13329 | | 22 pages, 4 figures | Abstract (click to expand)Ease of access to data, tools and models expedites scientific research. In structural biology there are now numerous open repositories of experimental and simulated datasets. Being able to easily access and utilise these is crucial for allowing researchers to make optimal use of their research effort. The tools presented here are useful for collating existing public cryoEM datasets and/or creating new synthetic cryoEM datasets to aid the development of novel data processing and interpretation algorithms. In recent years, structural biology has seen the development of a multitude of machine-learning based algorithms for aiding numerous steps in the processing and reconstruction of experimental datasets and the use of these approaches has become widespread. Developing such techniques in structural biology requires access to large datasets which can be cumbersome to curate and unwieldy to make use of. In this paper we present a suite of Python software packages which we collectively refer to as PERC (profet, EMPIARreader and CAKED). These are designed to reduce the burden which data curation places upon structural biology research. The protein structure fetcher (profet) package allows users to conveniently download and cleave sequences or structures from the Protein Data Bank or Alphafold databases. EMPIARreader allows lazy loading of Electron Microscopy Public Image Archive datasets in a machine-learning compatible structure. The Class Aggregator for Key Electron-microscopy Data (CAKED) package is designed to seamlessly facilitate the training of machine learning models on electron microscopy data, including electron-cryo-microscopy-specific data augmentation and labelling. These packages may be utilised independently or as building blocks in workflows. All are available in open source repositories and designed to be easily extensible to facilitate more advanced workflows if required. |
| 2025-03-17 | Magneto-thermally Coupled Field Simulation of Homogenized Foil Winding Models | Silas Weinert, Jonas Bundschuh, Yvonne Späck-Leigsnering et.al. | 2503.13010 | | 6 pages, 8 figures | Abstract (click to expand)Foil windings have, due to their layered structure, different properties than conventional wire windings, which make them advantageous for high frequency applications. Both electromagnetic and thermal analyses are relevant for foil windings. These two physical areas are coupled through Joule losses and temperature dependent material properties. For an efficient simulation of foil windings, homogenization techniques are used to avoid resolving the single turns. Therefore, this paper comprises a coupled magneto-thermal simulation that uses a homogenization method in the electromagnetic and thermal part. A weak coupling with different time step sizes for both parts is presented. The method is validated on a simple geometry and showcased for a pot transformer that uses a foil and a wire winding. |
| 2025-03-17 | AUTV: Creating Underwater Video Datasets with Pixel-wise Annotations | Quang Trung Truong, Wong Yuk Kwan, Duc Thanh Nguyen et.al. | 2503.12828 | | under review | Abstract (click to expand)Underwater video analysis, hampered by the dynamic marine environment and camera motion, remains a challenging task in computer vision. Existing training-free video generation techniques, learning motion dynamics on the frame-by-frame basis, often produce poor results with noticeable motion interruptions and misaligments. To address these issues, we propose AUTV, a framework for synthesizing marine video data with pixel-wise annotations. We demonstrate the effectiveness of this framework by constructing two video datasets, namely UTV, a real-world dataset comprising 2,000 video-text pairs, and SUTV, a synthetic video dataset including 10,000 videos with segmentation masks for marine objects. UTV provides diverse underwater videos with comprehensive annotations including appearance, texture, camera intrinsics, lighting, and animal behavior. SUTV can be used to improve underwater downstream tasks, which are demonstrated in video inpainting and video object segmentation. |
| 2025-03-17 | Cohort-attention Evaluation Metric against Tied Data: Studying Performance of Classification Models in Cancer Detection | Longfei Wei, Fang Sheng, Jianfei Zhang et.al. | 2503.12755 | | | Abstract (click to expand)Artificial intelligence (AI) has significantly improved medical screening accuracy, particularly in cancer detection and risk assessment. However, traditional classification metrics often fail to account for imbalanced data, varying performance across cohorts, and patient-level inconsistencies, leading to biased evaluations. We propose the Cohort-Attention Evaluation Metrics (CAT) framework to address these challenges. CAT introduces patient-level assessment, entropy-based distribution weighting, and cohort-weighted sensitivity and specificity. Key metrics like CATSensitivity (CATSen), CATSpecificity (CATSpe), and CATMean ensure balanced and fair evaluation across diverse populations. This approach enhances predictive reliability, fairness, and interpretability, providing a robust evaluation method for AI-driven medical screening models. |
| 2025-03-16 | Discovering uncertainty: Gaussian constitutive neural networks with correlated weights | Jeremy A. McCulloch, Ellen Kuhl et.al. | 2503.12679 | link | 10 pages, 5 figures, 1 table | Abstract (click to expand)When characterizing materials, it can be important to not only predict their mechanical properties, but also to estimate the probability distribution of these properties across a set of samples. Constitutive neural networks allow for the automated discovery of constitutive models that exactly satisfy physical laws given experimental testing data, but are only capable of predicting the mean stress response. Stochastic methods treat each weight as a random variable and are capable of learning their probability distributions. Bayesian constitutive neural networks combine both methods, but their weights lack physical interpretability and we must sample each weight from a probability distribution to train or evaluate the model. Here we introduce a more interpretable network with fewer parameters, simpler training, and the potential to discover correlated weights: Gaussian constitutive neural networks. We demonstrate the performance of our new Gaussian network on biaxial testing data, and discover a sparse and interpretable four-term model with correlated weights. Importantly, the discovered distributions of material parameters across a set of samples can serve as priors to discover better constitutive models for new samples with limited data. We anticipate that Gaussian constitutive neural networks are a natural first step towards generative constitutive models informed by physical laws and parameter uncertainty. |
| 2025-03-16 | Modeling ice cliff stability using a new Mohr-Coulomb-based phase field fracture model | T. Clayton, R. Duddu, T. Hageman et.al. | 2503.12481 | | | Abstract (click to expand)Iceberg calving at glacier termini results in mass loss from ice sheets, but the associated fracture mechanics is often poorly represented using simplistic (empirical or elementary mechanics-based) failure criteria. Here, we propose an advanced Mohr-Coulomb failure criterion that drives cracking based on the visco-elastic stress state in ice. This criterion is implemented in a phase field fracture framework, and finite element simulations are conducted to determine the critical conditions that can trigger ice cliff collapse. Results demonstrate that fast-moving glaciers with negligible basal friction are prone to tensile failure causing crevasse propagation far away from the ice front; whilst slow-moving glaciers with significant basal friction are likely to exhibit shear failure near the ice front. Results also indicate that seawater pressure plays a major role in modulating cliff failure. For land terminating glaciers, full thickness cliff failure is observed if the glacier exceeds a critical height, dependent on cohesive strength \(\tau_\mathrm{c}\) (\(H \approx 120\;\text{m}\) for \(\tau_\mathrm{c}=0.5\;\text{MPa}\)). For marine-terminating glaciers, ice cliff failure occurs if a critical glacier free-board (\(H-h_\mathrm{w}\)) is exceeded, with ice slumping only observed above the ocean-water height; for \(\tau_\mathrm{c} = 0.5\;\text{MPa}\), the model-predicted critical free-board is \(H-h_\mathrm{w} \approx 215\;\text{m}\) , which is in good agreement with field observations. While the critical free-board height is larger than that predicted by some previous models, we cannot conclude that marine ice cliff instability is less likely because we do not include other failure processes such as hydrofracture of basal crevasses and plastic necking. |
| 2025-03-16 | Development of a Cost-Effective Simulation Tool for Loss of Flow Accident Transients in High-Temperature Gas-cooled Reactors | Bo Liu, Wei Wang, Charles Moulinec et.al. | 2503.12467 | | | Abstract (click to expand)The aim of this work is to further expand the capability of the coarse-grid Computational Fluid Dynamics (CFD) approach, SubChCFD, to effectively simulate transient and buoyancy-influenced flows, which are critical in accident analyses of High-Temperature Gas-cooled Reactors (HTGRs). It has been demonstrated in our previous work that SubChCFD is highly adaptable to HTGR fuel designs and performs exceptionally well in modelling steady-state processes. In this study, the approach is extended to simulate a Loss of Flow Accident (LOFA) transient, where coolant circulation is disrupted, causing the transition from forced convection to buoyancy-driven natural circulation within the reactor core. To enable SubChCFD to capture the complex physics involved, corrections were introduced to the empirical correlations to account for the effects of flow unsteadiness, property variation and buoyancy. A 1/12th sector of the reactor core, representing the smallest symmetric unit, was modelled using a coarse mesh of approximately 60 million cells. This mesh size is about 6% of that required for a Reynolds Averaged Navier Stokes (RANS) model, where mesh sizes can typically reach the order of 1 billion cells for such configurations. Simulation results show that SubChCFD effectively captures the thermal hydraulic behaviours of the reactor during a LOFA transient, producing predictions in good agreement with RANS simulations while significantly reducing computational cost. |
| 2025-03-14 | Adiabatic Flame Temperatures for Oxy-Methane, Oxy-Hydrogen, Air-Methane, and Air-Hydrogen Stoichiometric Combustion using the NASA CEARUN Tool, GRI-Mech 3.0 Reaction Mechanism, and Cantera Python Package | Osama A. Marzouk et.al. | 2503.11826 | | 8 pages, 8 figures, 8 tables, peer-reviewed journal paper, open access | Abstract (click to expand)The Adiabatic Flame Temperature (AFT) in combustion represents the maximum attainable temperature at which the chemical energy in the reactant fuel is converted into sensible heat in combustion products without heat loss. AFT depends on the fuel, oxidizer, and chemical composition of the products. Computing AFT requires solving either a nonlinear equation or a larger minimization problem. This study obtained the AFTs for oxy-methane (methane and oxygen), oxy-hydrogen (hydrogen and oxygen), air-methane (methane and air), and air-hydrogen (hydrogen and air) for stoichiometric conditions. The reactant temperature was 298.15 K (25{\deg}C), and the pressure was kept constant at 1 atm. Two reaction mechanisms were attempted: a global single-step irreversible reaction for complete combustion and the GRI-Mech 3.0 elementary mechanism (53 species, 325 steps) for chemical equilibrium with its associated thermodynamic data. NASA CEARUN was the main modeling tool used. Two other tools were used for benchmarking: an Excel and a Cantera-Python implementation of GRI-Mech 3.0. The results showed that the AFTs for oxy-methane were 5,166.47 K (complete combustion) and 3,050.12 K (chemical equilibrium), and dropped to 2,326.35 K and 2,224.25 K for air-methane, respectively. The AFTs for oxy-hydrogen were 4,930.56 K (complete combustion) and 3,074.51 K (chemical equilibrium), and dropped to 2,520.33 K and 2,378.62 K for air-hydrogen, respectively. For eight combustion modeling cases, the relative deviation between the AFTs predicted by CEARUN and GRI-Mech 3.0 ranged from 0.064% to 3.503%. |
| 2025-03-14 | Unfitted hybrid high-order methods stabilized by polynomial extension for elliptic interface problems | Erik Burman, Alexandre Ern, Romain Mottier et.al. | 2503.11397 | | | Abstract (click to expand)In this work, we propose the design and the analysis of a novel hybrid high-order (HHO) method on unfitted meshes. HHO methods rely on a pair of unknowns, combining polynomials attached to the mesh faces and the mesh cells. In the unfitted framework, the interface can cut through the mesh cells in a very general fashion, and the polynomial unknowns are doubled in the cut cells and the cut faces. In order to avoid the ill-conditioning issues caused by the presence of small cut cells, the novel approach introduced herein is to use polynomial extensions in the definition of the gradient reconstruction operator. Stability and consistency results are established, leading to optimally decaying error estimates. The theory is illustrated by numerical experiments. |
| 2025-03-14 | Corrected Riemann smoothed particle hydrodynamics method for multi-resolution fluid-structure interaction | Bo Zhang, Jianfeng Zhu, Xiangyu Hu et.al. | 2503.11292 | link | 47 pages 19 figues | Abstract (click to expand)As a mesh-free method, smoothed particle hydrodynamics (SPH) has been widely used for modeling and simulating fluid-structure interaction (FSI) problems. While the kernel gradient correction (KGC) method is commonly applied in structural domains to enhance numerical consistency, high-order consistency corrections that preserve conservation remain underutilized in fluid domains despite their critical role in FSI analysis, especially for the multi-resolution scheme where fluid domains generally have a low resolution. In this study, we incorporate the reverse kernel gradient correction (RKGC) formulation, a conservative high-order consistency approximation, into the fluid discretization for solving FSI problems. RKGC has been proven to achieve exact second-order convergence with relaxed particles and improve numerical accuracy while particularly enhancing energy conservation in free-surface flow simulations. By integrating this correction into the Riemann SPH method to solve different typical FSI problems with a multi-resolution scheme, numerical results consistently show improvements in accuracy and convergence compared to uncorrected fluid discretization. Despite these advances, further refinement of correction techniques for solid domains and fluid-structure interfaces remains significant for enhancing the overall accuracy of SPH-based FSI modeling and simulation. |
| 2025-03-13 | Predicting Stock Movement with BERTweet and Transformers | Michael Charles Albada, Mojolaoluwa Joshua Sonola et.al. | 2503.10957 | | 9 pages, 4 figures, 2 tables | Abstract (click to expand)Applying deep learning and computational intelligence to finance has been a popular area of applied research, both within academia and industry, and continues to attract active attention. The inherently high volatility and non-stationary of the data pose substantial challenges to machine learning models, especially so for today's expressive and highly-parameterized deep learning models. Recent work has combined natural language processing on data from social media to augment models based purely on historic price data to improve performance has received particular attention. Previous work has achieved state-of-the-art performance on this task by combining techniques such as bidirectional GRUs, variational autoencoders, word and document embeddings, self-attention, graph attention, and adversarial training. In this paper, we demonstrated the efficacy of BERTweet, a variant of BERT pre-trained specifically on a Twitter corpus, and the transformer architecture by achieving competitive performance with the existing literature and setting a new baseline for Matthews Correlation Coefficient on the Stocknet dataset without auxiliary data sources. |
| 2025-03-13 | Design and Analysis of an Extreme-Scale, High-Performance, and Modular Agent-Based Simulation Platform | Lukas Johannes Breitwieser et.al. | 2503.10796 | | PhD Thesis submitted to ETH Zurich | Abstract (click to expand)Agent-based modeling is indispensable for studying complex systems across many domains. However, existing simulation platforms exhibit two major issues: performance and modularity. Low performance prevents simulations with a large number of agents, increases development time, limits parameter exploration, and raises computing costs. Inflexible software designs motivate modelers to create their own tools, diverting valuable resources. This dissertation introduces a novel simulation platform called BioDynaMo and its significant improvement, TeraAgent, to alleviate these challenges via three major works. First, we lay the platform's foundation by defining abstractions, establishing software infrastructure, and implementing a multitude of features for agent-based modeling. We demonstrate BioDynaMo's modularity through use cases in neuroscience, epidemiology, and oncology. We validate these models and show the simplicity of adding new functionality with few lines of code. Second, we perform a rigorous performance analysis and identify challenges for shared-memory parallelism. Provided solutions include an optimized grid for neighbor searching, mechanisms to reduce the memory access latency, and exploiting domain knowledge to omit unnecessary work. These improvements yield up to three orders of magnitude speedups, enabling simulations of 1.7 billion agents on a single server. Third, we present TeraAgent, a distributed simulation engine that allows scaling out the computation of one simulation to multiple servers. We identify and address server communication bottlenecks and implement solutions for serialization and delta encoding to accelerate and reduce data transfer. TeraAgent can simulate 500 billion agents and scales to 84096 CPU cores. BioDynaMo has been widely adopted, including a prize-winning radiotherapy simulation recognized as a top 10 breakthrough in physics in 2024. |
| 2025-03-13 | Unifying monitoring and modelling of water concentration levels in surface waters | Peter B Sorensen, Anders Nielsen, Peter E Holm et.al. | 2503.10285 | | 41 pages, 11 figures, Developed to support the Danish EPA | Abstract (click to expand)Accurate prediction of expected concentrations is essential for effective catchment management, requiring both extensive monitoring and advanced modeling techniques. However, due to limitations in the equation solving capacity, the integration of monitoring and modeling has been suffering suboptimal statistical approaches. This limitation results in models that can only partially leverage monitoring data, thus being an obstacle for realistic uncertainty assessments by overlooking critical correlations between both measurements and model parameters. This study presents a novel solution that integrates catchment monitoring and a unified hieratical statistical catchment modeling that employs a log-normal distribution for residuals within a left-censored likelihood function to address measurements below detection limits. This enables the estimation of concentrations within sub-catchments in conjunction with a source/fate sub-catchment model and monitoring data. This approach is possible due to a model builder R package denoted RTMB. The proposed approach introduces a statistical paradigm based on a hierarchical structure, capable of accommodating heterogeneous sampling across various sampling locations and the authors suggest that this also will encourage further refinement of other existing modeling platforms within the scientific community to improve synergy with monitoring programs. The application of the method is demonstrated through an analysis of nickel concentrations in Danish surface waters. |
| 2025-03-13 | A Neumann-Neumann Acceleration with Coarse Space for Domain Decomposition of Extreme Learning Machines | Chang-Ock Lee, Byungeun Ryoo et.al. | 2503.10032 | | 21 pages, 6 figures, 6 tables | Abstract (click to expand)Extreme learning machines (ELMs), which preset hidden layer parameters and solve for last layer coefficients via a least squares method, can typically solve partial differential equations faster and more accurately than Physics Informed Neural Networks. However, they remain computationally expensive when high accuracy requires large least squares problems to be solved. Domain decomposition methods (DDMs) for ELMs have allowed parallel computation to reduce training times of large systems. This paper constructs a coarse space for ELMs, which enables further acceleration of their training. By partitioning interface variables into coarse and non-coarse variables, selective elimination introduces a Schur complement system on the non-coarse variables with the coarse problem embedded. Key to the performance of the proposed method is a Neumann-Neumann acceleration that utilizes the coarse space. Numerical experiments demonstrate significant speedup compared to a previous DDM method for ELMs. |
| 2025-03-12 | A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networks | Faezeh Sarlakifar, Mohammadreza Mohammadzadeh Asl, Sajjad Rezvani Khaledi et.al. | 2503.09655 | | | Abstract (click to expand)Traditional Long Short-Term Memory (LSTM) networks are effective for handling sequential data but have limitations such as gradient vanishing and difficulty in capturing long-term dependencies, which can impact their performance in dynamic and risky environments like stock trading. To address these limitations, this study explores the usage of the newly introduced Extended Long Short Term Memory (xLSTM) network in combination with a deep reinforcement learning (DRL) approach for automated stock trading. Our proposed method utilizes xLSTM networks in both actor and critic components, enabling effective handling of time series data and dynamic market environments. Proximal Policy Optimization (PPO), with its ability to balance exploration and exploitation, is employed to optimize the trading strategy. Experiments were conducted using financial data from major tech companies over a comprehensive timeline, demonstrating that the xLSTM-based model outperforms LSTM-based methods in key trading evaluation metrics, including cumulative return, average profitability per trade, maximum earning rate, maximum pullback, and Sharpe ratio. These findings mark the potential of xLSTM for enhancing DRL-based stock trading systems. |
| 2025-03-18 | Leveraging LLMS for Top-Down Sector Allocation In Automated Trading | Ryan Quek Wei Heng, Edoardo Vittori, Keane Ong et.al. | 2503.09647 | | | Abstract (click to expand)This paper introduces a methodology leveraging Large Language Models (LLMs) for sector-level portfolio allocation through systematic analysis of macroeconomic conditions and market sentiment. Our framework emphasizes top-down sector allocation by processing multiple data streams simultaneously, including policy documents, economic indicators, and sentiment patterns. Empirical results demonstrate superior risk-adjusted returns compared to traditional cross momentum strategies, achieving a Sharpe ratio of 2.51 and portfolio return of 8.79% versus -0.61 and -1.39% respectively. These results suggest that LLM-based systematic macro analysis presents a viable approach for enhancing automated portfolio allocation decisions at the sector level. |
| 2025-03-12 | AI-based Framework for Robust Model-Based Connector Mating in Robotic Wire Harness Installation | Claudius Kienle, Benjamin Alt, Finn Schneider et.al. | 2503.09409 | | 6 pages, 6 figures, 4 tables, submitted to the 2025 IEEE 21st International Conference on Automation Science and Engineering | Abstract (click to expand)Despite the widespread adoption of industrial robots in automotive assembly, wire harness installation remains a largely manual process, as it requires precise and flexible manipulation. To address this challenge, we design a novel AI-based framework that automates cable connector mating by integrating force control with deep visuotactile learning. Our system optimizes search-and-insertion strategies using first-order optimization over a multimodal transformer architecture trained on visual, tactile, and proprioceptive data. Additionally, we design a novel automated data collection and optimization pipeline that minimizes the need for machine learning expertise. The framework optimizes robot programs that run natively on standard industrial controllers, permitting human experts to audit and certify them. Experimental validations on a center console assembly task demonstrate significant improvements in cycle times and robustness compared to conventional robot programming approaches. Videos are available under https://claudius-kienle.github.io/AppMuTT. |
| 2025-03-12 | Large-scale Thermo-Mechanical Simulation of Laser Beam Welding Using High-Performance Computing: A Qualitative Reproduction of Experimental Results | Tommaso Bevilacqua, Andrey Gumenyuk, Niloufar Habibi et.al. | 2503.09345 | | | Abstract (click to expand)Laser beam welding is a non-contact joining technique that has gained significant importance in the course of the increasing degree of automation in industrial manufacturing. This process has established itself as a suitable joining tool for metallic materials due to its non-contact processing, short cycle times, and small heat-affected zones. One potential problem, however, is the formation of solidification cracks, which particularly affects alloys with a pronounced melting range. Since solidification cracking is influenced by both temperature and strain rate, precise measurement technologies are of crucial importance. For this purpose, as an experimental setup, a Controlled Tensile Weldability (CTW) test combined with a local deformation measurement technique is used. The aim of the present work is the development of computational methods and software tools to numerically simulate the CTW. The numerical results are compared with those obtained from the experimental CTW. In this study, an austenitic stainless steel sheet is selected. A thermo-elastoplastic material behavior with temperature-dependent material parameters is assumed. The time-dependent problem is first discretized in time and then the resulting nonlinear problem is linearized with Newton's method. For the discretization in space, finite elements are used. In order to obtain a sufficiently accurate solution, a large number of finite elements has to be used. In each Newton step, this yields a large linear system of equations that has to be solved. Therefore, a highly parallel scalable solver framework, based on the software library PETSc, was used to solve this computationally challenging problem on a high-performance computing architecture. Finally, the experimental results and the numerical simulations are compared, showing to be qualitatively in good agreement. |
| 2025-03-12 | A 3d particle visualization system for temperature management | Benoit Lange, Nancy Rodriguez, William Puech et.al. | 2503.09198 | | | Abstract (click to expand)This paper deals with a 3D visualization technique proposed to analyze and manage energy efficiency from a data center. Data are extracted from sensors located in the IBM Green Data Center in Montpellier France. These sensors measure different information such as hygrometry, pressure and temperature. We want to visualize in real-time the large among of data produced by these sensors. A visualization engine has been designed, based on particles system and a client server paradigm. In order to solve performance problems, a Level Of Detail solution has been developed. These methods are based on the earlier work introduced by J. Clark in 1976. In this paper we introduce a particle method used for this work and subsequently we explain different simplification methods we have applied to improve our solution. |
| 2025-03-11 | Capturing Lifecycle System Degradation in Digital Twin Model Updating | Yifan Tang, Mostafa Rahmani Dehaghani, G. Gary Wang et.al. | 2503.08953 | | 32 pages, 25 figures | Abstract (click to expand)Digital twin (DT) has emerged as a powerful tool to facilitate monitoring, control, and other decision-making tasks in real-world engineering systems. Online update methods have been proposed to update DT models. Considering the degradation behavior in the system lifecycle, these methods fail to enable DT models to predict the system responses affected by the system degradation over time. To alleviate this problem, degradation models of measurable parameters have been integrated into DT construction. However, identifying the degradation parameters relies on prior knowledge of the system and expensive experiments. To mitigate those limitations, this paper proposes a lifelong update method for DT models to capture the effects of system degradation on system responses without any prior knowledge and expensive offline experiments on the system. The core idea in the work is to represent the system degradation during the lifecycle as the dynamic changes of DT configurations (i.e., model parameters with a fixed model structure) at all degradation stages. During the lifelong update process, an Autoencoder is adopted to reconstruct the model parameters of all hidden layers simultaneously, so that the latent features taking into account the dependencies among hidden layers are obtained for each degradation stage. The dynamic behavior of latent features among successive degradation stages is then captured by a long short-term memory model, which enables prediction of the latent feature at any unseen stage. Based on the predicted latent features, the model configuration at future degradation stage is reconstructed to determine the new DT model, which predicts the system responses affected by the degradation at the same stage. The test results on two engineering datasets demonstrate that the proposed update method could capture effects of system degradation on system responses during the lifecycle. |
| 2025-03-11 | Towards Efficient Parametric State Estimation in Circulating Fuel Reactors with Shallow Recurrent Decoder Networks | Stefano Riva, Carolina Introini, J. Nathan Kutz et.al. | 2503.08904 | link | arXiv admin note: text overlap with arXiv:2409.12550 | Abstract (click to expand)The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering systems; nuclear reactors represent particularly challenging applications for this task due to the complexity of the strongly coupled physics involved and the extremely harsh and hostile environments, especially for new technologies such as Generation-IV reactors. Data-driven techniques can combine different sources of information, including computational proxy models and local noisy measurements on the system, to robustly estimate the state. This work leverages the novel Shallow Recurrent Decoder architecture to infer the entire state vector (including neutron fluxes, precursors concentrations, temperature, pressure and velocity) of a reactor from three out-of-core time-series neutron flux measurements alone. In particular, this work extends the standard architecture to treat parametric time-series data, ensuring the possibility of investigating different accidental scenarios and showing the capabilities of this approach to provide an accurate state estimation in various operating conditions. This paper considers as a test case the Molten Salt Fast Reactor (MSFR), a Generation-IV reactor concept, characterised by strong coupling between the neutronics and the thermal hydraulics due to the liquid nature of the fuel. The promising results of this work are further strengthened by the possibility of quantifying the uncertainty associated with the state estimation, due to the considerably low training cost. The accurate reconstruction of every characteristic field in real-time makes this approach suitable for monitoring and control purposes in the framework of a reactor digital twin. |
| 2025-03-11 | Nonlinear optimals and their role in sustaining turbulence in channel flow | Dario Klingenberg, Rich R. Kerswell et.al. | 2503.08283 | link | | Abstract (click to expand)We investigate the energy transfer from the mean profile to velocity fluctuations in channel flow by calculating nonlinear optimal disturbances,i.e. the initial condition of a given finite energy that achieves the highest possible energy growth during a given fixed time horizon. It is found that for a large range of time horizons and initial disturbance energies, the nonlinear optimal exhibits streak spacing and amplitude consistent with DNS at least at Re_tau = 180, which suggests that they isolate the relevant physical mechanisms that sustain turbulence. Moreover, the time horizon necessary for a nonlinear disturbance to outperform a linear optimal is consistent with previous DNS-based estimates using eddy turnover time, which offers a new perspective on how some turbulent time scales are determined. |
| 2025-03-11 | XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change | Jiawen Wei, Aniruddha Bora, Vivek Oommen et.al. | 2503.08163 | | | Abstract (click to expand)Extreme weather events are increasing in frequency and intensity due to climate change. This, in turn, is exacting a significant toll in communities worldwide. While prediction skills are increasing with advances in numerical weather prediction and artificial intelligence tools, extreme weather still present challenges. More specifically, identifying the precursors of such extreme weather events and how these precursors may evolve under climate change remain unclear. In this paper, we propose to use post-hoc interpretability methods to construct relevance weather maps that show the key extreme-weather precursors identified by deep learning models. We then compare this machine view with existing domain knowledge to understand whether deep learning models identified patterns in data that may enrich our understanding of extreme-weather precursors. We finally bin these relevant maps into different multi-year time periods to understand the role that climate change is having on these precursors. The experiments are carried out on Indochina heatwaves, but the methodology can be readily extended to other extreme weather events worldwide. |
| 2025-03-10 | Network Analysis of Uniswap: Centralization and Fragility in the Decentralized Exchange Market | Tao Yan, Claudio J. Tessone et.al. | 2503.07834 | | | Abstract (click to expand)The Uniswap is a Decentralized Exchange (DEX) protocol that facilitates automatic token exchange without the need for traditional order books. Every pair of tokens forms a liquidity pool on Uniswap, and each token can be paired with any other token to create liquidity pools. This characteristic motivates us to employ a complex network approach to analyze the features of the Uniswap market. This research presents a comprehensive analysis of the Uniswap network using complex network methods. The network on October 31, 2023, is built to observe its recent features, showcasing both scale-free and core-periphery properties. By employing node and edge-betweenness metrics, we detect the most important tokens and liquidity pools. Additionally, we construct daily networks spanning from the beginning of Uniswap V2 on May 5, 2020, until October 31, 2023, and our findings demonstrate that the network becomes increasingly fragile over time. Furthermore, we conduct a robustness analysis by simulating the deletion of nodes to estimate the impact of some extreme events such as the Terra collapse. The results indicate that the Uniswap network exhibits robustness, yet it is notably fragile when deleting tokens with high betweenness centrality. This finding highlights that, despite being a decentralized exchange, Uniswap exhibits significant centralization tendencies in terms of token network connectivity and the distribution of TVL across nodes (tokens) and edges (liquidity pools). |
| 2025-03-10 | What is missing from existing Lithium-Sulfur models to capture coin-cell behaviour? | Miss. Elizabeth Olisa Monica Marinescu et.al. | 2503.07684 | | 27 pages, 7 figures, conferences presented: ModVal 2025, ECS 2025 | Abstract (click to expand)Lithium-sulfur (Li-S) batteries offer a promising alternative to current lithium-ion (Li-ion) batteries, with a high theoretical energy density, improved safety and high abundance, low cost of materials. For Li-S to reach commercial application, it is essential to understand how the behaviour scales between cell formats; new material development is predominately completed at coin-cell level, whilst pouch-cells will be used for commercial applications. Differences such as reduced electrolyte-to-sulfur (E/S) ratios and increased geometric size at larger cell formats contribute to the behavioural differences, in terms of achievable capacity, cyclability and potential degradation mechanisms. This work focuses on the steps required to capture and test coin-cell behaviour, building upon the existing models within the literature, which predominately focus on pouch-cells. The areas investigated throughout this study, to improve the capability of the model in terms of scaling ability and causality of predictions, include the cathode surface area, precipitation dynamics and C-rate dependence. |
| 2025-03-10 | Simultaneous Energy Harvesting and Bearing Fault Detection using Piezoelectric Cantilevers | P. Peralta-Braz, M. M. Alamdari, C. T. Chou et.al. | 2503.07462 | | | Abstract (click to expand)Bearings are critical components in industrial machinery, yet their vulnerability to faults often leads to costly breakdowns. Conventional fault detection methods depend on continuous, high-frequency vibration sensing, digitising, and wireless transmission to the cloud-an approach that significantly drains the limited energy reserves of battery-powered sensors, accelerating their depletion and increasing maintenance costs. This work proposes a fundamentally different approach: rather than using instantaneous vibration data, we employ piezoelectric energy harvesters (PEHs) tuned to specific frequencies and leverage the cumulative harvested energy over time as the key diagnostic feature. By directly utilising the energy generated from the machinery's vibrations, we eliminate the need for frequent analog-to-digital conversions and data transmission, thereby reducing energy consumption at the sensor node and extending its operational lifetime. To validate this approach, we use a numerical PEH model and publicly available acceleration datasets, examining various PEH designs with different natural frequencies. We also consider the influence of the classification algorithm, the number of devices, and the observation window duration. The results demonstrate that the harvested energy reliably indicates bearing faults across a range of conditions and severities. By converting vibration energy into both a power source and a diagnostic feature, our solution offers a more sustainable, low-maintenance strategy for fault detection in smart machinery. |
| 2025-03-10 | Early signs of stuck pipe detection based on Crossformer | Bo Cao, Yu Song, Jin Yang et.al. | 2503.07440 | | 33 pages,9 figure | Abstract (click to expand)Stuck pipe incidents are one of the major challenges in drilling engineering,leading to massive time loss and additional costs.To address the limitations of insufficient long sequence modeling capability,the difficulty in accurately establishing warning threshold,and the lack of model interpretability in existing methods,we utilize Crossformer for early signs of detection indicating potential stuck events in order to provide guidance for on-site drilling engineers and prevent stuck pipe incidents.The sliding window technique is integrated into Crossformer to allow it to output and display longer outputs,the improved Crossformer model is trained using normal time series drilling data to generate predictions for various parameters at each time step.The relative reconstruction error of model is regard as the risk of stuck pipe,thereby considering data that the model can't predict as anomalies,which represent the early signs of stuck pipe incidents.The multi-step prediction capability of Crossformer and relative reconstruction error are combined to assess stuck pipe risk at each time step in advance.We partition the reconstruction error into modeling error and error due to anomalous data fluctuations,furthermore,the dynamic warning threshold and warning time for stuck pipe incidents are determined using the probability density function of reconstruction errors from normal drilling data.The results indicate that our method can effectively detect early signs of stuck pipe incidents during the drilling process.Crossformer exhibits superior modeling and predictive capabilities compared with other deep learning models.Transformer-based models with multi-step prediction capability are more suitable for stuck pipe prediction compared to the current single-step prediction models. |
| 2025-03-10 | An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning | Ge Zheng, Alexandra Brintrup et.al. | 2503.07231 | | 15 pages, 5 figures, 5 tables, submitted to a journal | Abstract (click to expand)In today's globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabilities, highlighted by recent global crises, underscore the urgent need for improved visibility and resilience of the supply chain. However, data-sharing limitations often hinder the achievement of comprehensive visibility between organisations or countries due to privacy, security, and regulatory concerns. Moreover, most existing research studies focused on individual firm- or product-level networks, overlooking the multifaceted interactions among diverse entities that characterise real-world supply chains, thus limiting a holistic understanding of supply chain dynamics. To address these challenges, we propose a novel approach that integrates Federated Learning (FL) and Graph Convolutional Neural Networks (GCNs) to enhance supply chain visibility through relationship prediction in supply chain knowledge graphs. FL enables collaborative model training across countries by facilitating information sharing without requiring raw data exchange, ensuring compliance with privacy regulations and maintaining data security. GCNs empower the framework to capture intricate relational patterns within knowledge graphs, enabling accurate link prediction to uncover hidden connections and provide comprehensive insights into supply chain networks. Experimental results validate the effectiveness of the proposed approach, demonstrating its ability to accurately predict relationships within country-level supply chain knowledge graphs. This enhanced visibility supports actionable insights, facilitates proactive risk management, and contributes to the development of resilient and adaptive supply chain strategies, ensuring that supply chains are better equipped to navigate the complexities of the global economy. |
| 2025-03-10 | Simulating programmable morphing of shape memory polymer beam systems with complex geometry and topology | Giulio Ferri, Enzo Marino et.al. | 2503.07150 | | | Abstract (click to expand)We propose a novel approach to the analysis of programmable geometrically exact shear deformable beam systems made of shape memory polymers. The proposed method combines the viscoelastic Generalized Maxwell model with the Williams, Landel and Ferry relaxation principle, enabling the reproduction of the shape memory effect of structural systems featuring complex geometry and topology. Very high efficiency is pursued by discretizing the differential problem in space through the isogeometric collocation (IGA-C) method. The method, in addition to the desirable attributes of isogeometric analysis (IGA), such as exactness of the geometric reconstruction of complex shapes and high-order accuracy, circumvents the need for numerical integration since it discretizes the problem in the strong form. Other distinguishing features of the proposed formulation are: i) \({\rm SO}(3)\) -consistency for the linearization of the problem and for the time stepping; ii) minimal (finite) rotation parametrization, that means only three rotational unknowns are used; iii) no additional unknowns are needed to account for the rate-dependent material compared to the purely elastic case. Through different numerical applications involving challenging initial geometries, we show that the proposed formulation possesses all the sought attributes in terms of programmability of complex systems, geometric flexibility, and high order accuracy. |
| 2025-03-10 | Effect of Selection Format on LLM Performance | Yuchen Han, Yucheng Wu, Jeffrey Willard et.al. | 2503.06926 | | | Abstract (click to expand)This paper investigates a critical aspect of large language model (LLM) performance: the optimal formatting of classification task options in prompts. Through an extensive experimental study, we compared two selection formats -- bullet points and plain English -- to determine their impact on model performance. Our findings suggest that presenting options via bullet points generally yields better results, although there are some exceptions. Furthermore, our research highlights the need for continued exploration of option formatting to drive further improvements in model performance. |
| 2025-03-09 | Modular Photobioreactor Façade Systems for Sustainable Architecture: Design, Fabrication, and Real-Time Monitoring | Xiujin Liu et.al. | 2503.06769 | | 21 pages, 22 figures, 3 tables | Abstract (click to expand)This paper proposes an innovative solution to the growing issue of greenhouse gas emissions: a closed photobioreactor (PBR) fa\c{c}ade system to mitigate greenhouse gas (GHG) concentrations. With digital fabrication technology, this study explores the transition from traditional, single function building facades to multifunctional, integrated building systems. It introduces a photobioreactor (PBR) fa\c{c}ade system to mitigate greenhouse gas (GHG) concentrations while addressing the challenge of large-scale prefabricated components transportation. This research introduces a novel approach by designing the fa\c{c}ade system as modular, user-friendly and transportation-friendly bricks, enabling the creation of a user-customized and self-assembled photobioreactor (PBR) system. The single module in the system is proposed to be "neutralization bricks", which embedded with algae and equipped with an air circulation system, facilitating the photobioreactor (PBR)'s functionality. A connection system between modules allows for easy assembly by users, while a limited variety of brick styles ensures modularity in manufacturing without sacrificing customization and diversity. The system is also equipped with an advanced microalgae status detection algorithm, which allows users to monitor the condition of the microalgae using monocular camera. This functionality ensures timely alerts and notifications for users to replace the algae, thereby optimizing the operational efficiency and sustainability of the algae cultivation process. |
| 2025-03-09 | Energy-Adaptive Checkpoint-Free Intermittent Inference for Low Power Energy Harvesting Systems | Sahidul Islam, Wei Wei, Jishnu Banarjee et.al. | 2503.06663 | | | Abstract (click to expand)Deep neural network (DNN) inference in energy harvesting (EH) devices poses significant challenges due to resource constraints and frequent power interruptions. These power losses not only increase end-to-end latency, but also compromise inference consistency and accuracy, as existing checkpointing and restore mechanisms are prone to errors. Consequently, the quality of service (QoS) for DNN inference on EH devices is severely impacted. In this paper, we propose an energy-adaptive DNN inference mechanism capable of dynamically transitioning the model into a low-power mode by reducing computational complexity when harvested energy is limited. This approach ensures that end-to-end latency requirements are met. Additionally, to address the limitations of error-prone checkpoint-and-restore mechanisms, we introduce a checkpoint-free intermittent inference framework that ensures consistent, progress-preserving DNN inference during power failures in energy-harvesting systems. |
