Scientific ML

ML: Machine Learning

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Overview

Scientific machine learning studies how to use machine learning for scientific prediction, simulation, and discovery while accounting for governing equations, physical constraints, and measurement processes. Common approaches include physics-informed neural networks, neural operators, hybrid models that augment classical solvers, and symbolic methods for learning equations; differentiable solvers and uncertainty estimates support inference and decision-making. Applications span problems such as fluid dynamics, climate, chemistry, materials, and astronomy, often where data are sparse or simulations are expensive. A central challenge is that physical structure can improve reliability when correctly specified but mislead when it does not fit the system, so predictive accuracy must be weighed against physical fidelity, generalization, uncertainty, and careful validation.

Latest papers

CardsList

125 papers11 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

  1. AI models of unstable flow exhibit hallucination

    Apr 22, 2026Ramdhan Wibawa, Birendra JhaScientific MLNeural Operators

    Department of Chemical Engineering and Materials Science, University of Southern California, 925 Bloom Walk, Los Angeles, 90089, CA, USA.

  2. Machine learning moment closure models for the radiative transfer equation IV: enforcing symmetrizable hyperbolicity in two dimensions

    Apr 22, 2026Juntao HuangReduced-Order ModelingScientific ML

    Department of Mathematical Sciences, University of Delaware, Newark, DE, 19716, USA.

  3. Design Rules for Extreme-Edge Scientific Computing on AI Engines

    Apr 21, 2026Zhenghua Ma, G Abarajithan, Dimitrios Danopoulos +3Scientific MLEdge ML

    University of California San Diego, La Jolla, CA, USA · European Organization for Nuclear Research (CERN), Geneva, Switzerland

  4. Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning

    Apr 17, 2026Reza PirayeshshirazinezhadSelf-Supervised LearningAdaptive Loss Weighting

    Texas A&M University College Station, TX, USA

  5. Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems

    Apr 13, 2026Gia-Wei Chern, Yunhao Fan, Sheng Zhang +1Scientific MLMaterials Science

    Department of Physics, University of Virginia, Charlottesville, VA 22904, USA

  6. Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics

    Apr 10, 2026Pablo Mercader-Perez, Carolina Cuesta-Lazaro, Daniel Muthukrishna +5AstrophysicsMultimodal Disentangled Representation Learning

    Massachusetts Institute of Technology · Flatiron Institute, Simons Foundation · Institute for Advanced Studies +5

  7. Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies

    Apr 7, 2026Prashant Kumar, Rajesh RanjanDomain Decomposition MethodsNeural PDE Solvers

    Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, India 208016

  8. Learning to Unscramble Feynman Loop Integrals with SAILIR

    Apr 6, 2026David ShihScientific MLMemory-Efficient Inference

    NHETC, Dept. of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854, USA

  9. Informed Machine Learning with Knowledge Landmarks

    Mar 31, 2026Chuyi Dai, Witold Pedrycz, Suping Xu +2Scientific ML

    Department of Electrical and Computer Engineering, University of Alberta, Edmonton, T6G 1H9, Canada · School of ElectroMechanical Engineering, Xidian University, Xi’an 710071, China · Hubei Subsurface Multi-Scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China

  10. Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning

    Mar 25, 2026Salah A Faroughi, Farinaz Mostajeran, Amirhossein Arzani +1Interpretable MLScientific ML

    Energy & Intelligence Lab, Department of Chemical Engineering, University of Utah, Salt Lake City, Utah, 84112, USA · Department of Mechanical Engineering, University of Utah, Salt Lake City, Utah, 84112, USA · Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, 84112, USA +2

  11. CFNN: Continued Fraction Neural Network

    Mar 21, 2026Chao Wang, Xuancheng Zhou, Ruilin Hou +2Function ApproximationScientific ML

    School of Future Technology, Shanghai University, Shanghai, 200444, China. · Institute of Artificial Intelligence, Shanghai University, Shanghai, 200444, China. · School of Computer Science and Technology, Shanghai University, Shanghai, 200444, China. +2

  12. How Can Machine Learning Emulators Best Support Climate Science?

    Mar 20, 2026Luca Schmidt, Nina Effenberger, Vitus Benson +5Surrogate ModelingClimate Science

    Institute for Atmospheric and Climate Science ETH Zurich · Seminar for Statistics ETH Zurich

  13. scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns

    Mar 18, 2026Sergey V. SamsonauStatic Code AnalysisScientific ML

    Authentic Research Partners, Princeton, NJ

  14. Bridging Theory and Data: Correcting Nuclear Mass Models with Interpretable Machine Learning

    Mar 16, 2026Yanhua Lu, Tianshuai Shang, Pengxiang Du +2Interpretable MLScientific ML

    College of Physics, Jilin University, Changchun 130012, China. · Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan · RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Wako 351-0198, Japan

  15. Machine learning cannot decarbonise the subsurface until it can be audited

    Mar 16, 2026Hannah P. Menke, Ahmed H. Elsheikh, Lingli Wei +2Scientific MLPhysics-Informed ML

    Institute of GeoEnergy Engineering, Heriot-Watt University, Edinburgh, UK · Subsurface Energy Transition and Innovation Centre, Heriot-Watt University, Edinburgh, UK · SubsurfaceLabs, Abu Dhabi, UAE +1

  16. A Score Filter Enhanced Data Assimilation Framework for Data-Driven Dynamical Systems

    Mar 16, 2026Jingqiao Tang, Ryan Bausback, Feng Bao +2Data AssimilationScientific ML

  17. From Specification to Architecture: A Theory Compiler for Knowledge-Guided Machine Learning

    Mar 15, 2026Asela Hevapathige, Yu Xia, Sachith Seneviratne +1Scientific ML

    AI, Optimization and Pattern Recognition Research Group, Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Australia

  18. An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process

    Mar 13, 2026Nicholas M. Synovic, Karolina Ryzka, Alessandra V. Vellucci Solari +3AI-Assisted Scientific ResearchTransfer Learning

    Computer Science Department, Loyola University Chicago, Chicago, IL, USA · Electrical and Computer Engineering Department, Purdue University, West Lafayette, IN, USA

  19. Representation Learning for Spatiotemporal Physical Systems

    Mar 13, 2026Helen Qu, Rudy Morel, Michael McCabe +4Representation LearningScientific ML

    Flatiron Institute · Princeton University · Université Paris-Saclay, Université Paris Cité, CEA, CNRS, AIM +1

  20. Differentiable Thermodynamic Phase-Equilibria for Machine Learning

    Mar 11, 2026Karim K. Ben Hicham, Moreno Ascani, Jan G. Rittig +1Scientific MLDifferentiable Programming

    RWTH Aachen University, Process Systems Engineering (AVT.SVT), 52074 Aachen, Germany · Forschungszentrum Jülich GmbH, Institute of Climate and Energy Systems ICE-1: Energy Systems Engineering, 52425 Jülich, Germany · JARA-ENERGY, 52056 Aachen, Germany

  21. Two Teachers Better Than One: Hardware-Physics Co-Guided Distributed Scientific Machine Learning

    Mar 10, 2026Yuchen Yuan, Junhuan Yang, Hao Wan +4Scientific MLEdge ML

    George Mason University, Fairfax, VA, USA · University of North Carolina at Chapel Hill, NC, USA

  22. Partial Differential Equations in the Age of Machine Learning: A Critical Synthesis of Classical, Machine Learning, and Hybrid Methods

    Mar 8, 2026Mohammad Nooraiepour, Jakub Wiktor Both, Teeratorn Kadeethum +1PDE SolvingPartial Differential Equations

    Faculty of Mathematics and Natural Sciences, University of Oslo, 1047 Blindern, 0316 Oslo, Norway · Department of Mathematics, University of Bergen, Bergen, Norway · AI Lab, Siemens Energy, Orlando, FL 32826, USA +1

  23. LUMINA: Foundation Models for Topology Transferable ACOPF

    Mar 4, 2026Yijiang Li, Zeeshan Memon, Hongwei Jin +7Physical Foundation ModelsScientific ML

    Argonne National Laboratory, Lemont, IL 60439, USA · Emory University, Atlanta, GA 30322, USA · Sogang University, Seoul, South Korea

  24. Adaptive Sensing of Continuous Physical Systems for Machine Learning

    Mar 4, 2026Felix Köster, Atsushi UchidaAdaptive SensingActive Perception

    Department of Information and Computer Sciences, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama City, Saitama, 338–8570, Japan

  25. MultiPUFFIN: A Multimodal Domain-Constrained Foundation Model for Molecular Property Prediction of Small Molecules

    Mar 1, 2026Idelfonso B. R. Nogueira, Carine M. Rebello, Mumin Enis Leblebici +1Molecular Property PredictionMultimodal Foundation Models

    Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, 7034, Norway · Faculty of Industrial Engineering, KU Leuven, Diepenbeek, Belgium · University of Surrey, Guildford, United Kingdom

  26. Neural Operators Can Discover Functional Clusters

    Feb 26, 2026Yicen Li, Jose Antonio Lara Benitez, Ruiyang Hong +3ClusteringUnsupervised Clustering

    McMaster University and the Vector Institute, Main St., Hamilton, L8S 4L8, Ontario, Canada · Rice University, Main St., Houston, 77005, Texas, United States

  27. LUMOS: Democratizing SciML Workflows with L0-Regularized Learning for Unified Feature and Parameter Adaptation

    Feb 26, 2026Shouwei Gao, Xu Zheng, Dongsheng Luo +2Scientific MLAutomated Machine Learning

    Oregon State University · Florida International University · Argonne National Laboratory

  28. Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data

    Feb 23, 2026Zhenyao Ma, Yue Liang, Dongxu LiInterpretable MLLearning to Optimize

    Xiamen University · University of Tübingen · Xi’an Jiaotong University

  29. Clifford Kolmogorov-Arnold Networks

    Feb 5, 2026Matthias Wolff, Francesco Alesiani, Christof Duhme +1Kolmogorov-Arnold NetworksNeural Network Approximation Theory

    Dept. of Computer Science University of Münster Münster, Germany · Data Science Div. NEC Labs Europe Heidelberg, Germany

  30. OpFML: Pipeline for ML-based Operational Inference

    Jan 16, 2026Shahbaz Alvi, Giusy Fedele, Gabriele Accarino +3Wildfire ForecastingScientific ML

    CMCC Foundation - Euro-Mediterranean Center on Climate Change, Lecce, 73100, Italy · Columbia University, Department of Earth and Environmental Engineering, New York, USA · University of Salento,Department of Innovation Engineering, Lecce, 73100, Italy