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. Machine learning applications in the heliosphere

    Oct 27, 2026Vishal Upendran, Banafsheh FerdousiScientific ML

    SETI Institute, Mountain View, CA, USA 94043 · Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, USA · Air Force Research Lab, Albuquerque, New Mexico, USA

  2. Generative Refinement Learning for Temporal Interpolation in Scientific Machine Learning

    Oct 27, 2026Pu Ren, Michael W. Mahoney, N. Benjamin ErichsonScientific MLResidual Learning

    Lawrence Berkeley National Lab · International Computer Science Institute · University of California, Berkeley

  3. Distilling neural networks into equations to predict planetary instability

    Oct 26, 2026Simon Alford, Edward Gu, Omar Costilla-Reyes +3Dynamical SystemsScientific ML

    Department of Computer Science, Cornell University, Ithaca, NY 14850, USA · Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA · Department of Physics, Harvey Mudd College, Claremont, CA 91711, USA +3

  4. Multi-Fidelity Training Reshapes Uncertainty Decomposition in Atomistic Models

    Oct 26, 2026Matteo Tolloso, Davide Bacciu, Luigi BonatiMultifidelity ModelingUncertainty Calibration

    Department of Computer Science, University of Pisa · Atomistic Simulations, Italian Institute of Technology

  5. Machine Learning Applications in the Solar Interior and Atmosphere

    Oct 26, 2026Vishal Upendran, Carlos J. Díaz BasoSolar PhysicsScientific ML

    SETI Institute, Mountain View, CA, USA 94043 · Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, USA 94304 · Institute of Theoretical Astrophysics, University of Oslo, P.O. Box 1029 Blindern, N-0315 Oslo, Norway +1

  6. SKG in Practice: OOD Generalization Cases Across FourDomains

    Oct 23, 2026Jian An, Chaoshi Wu, Xinpan LuoScientific MLOOD Generalization

  7. Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?

    Oct 20, 2026Ming Zhong, Stacy Li, Nicholas Carlini +1AI Agents for Scientific DiscoveryScientific Agent Benchmarks

    Anthropic Fellows Program · Anthropic

  8. Beyond DFT electronic band structures via dual-space orbital learning

    Oct 14, 2026Minhyuk Kang, Beom Seok Kang, Nikolai Argatoff +4Computational Materials ScienceMaterials Property Prediction

    Division of Engineering and Applied Science, California Institute of Technology · Department of Chemical Engineering, Imperial College London · Division of Chemistry and Chemical Engineering, California Institute of Technology +1

  9. Machine Learning Meets High-Energy Nuclear Physics: From Pattern Recognition to Physics-Integrated Discovery

    Oct 8, 2026Xun Chen, Weiyao Ke, Yu-Gang Ma +2Lattice Field TheoryHigh-Energy Physics

    School of Nuclear Science and Technology, University of South China, Hengyang 421001, China · INFN — Istituto Nazionale di Fisica Nucleare — Sezione di Bari, Via Orabona 4, 70125 Bari, Italy · Key Laboratory of Quark and Lepton Physics (MOE) & Institute of Particle Physics, Central China Normal University, Wuhan, Hubei 430079, China +6

  10. A neural density functional for polymer electrolytes with exact structure and transferable Coulomb coupling

    Oct 8, 2026Liyao LyuDensity Functional TheoryMaterials Science

    School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, Anhui, China · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China · Suzhou Big Data & AI Research and Engineering Center, Suzhou, Jiangsu, China

  11. Data, Numbers, and Geometry: Three Tutorials on Numerical Methods, Machine Learning, and Evaluation

    Oct 5, 2026Jessica N. Howard, Yidi Qi, Tomás S. R. SilvaScientific ML

    Instituto de Matemática, Estatística e Computação Científica (IMECC), Universidade Estadual de Campinas (UNICAMP), Rua Sérgio Buarque de Holanda, 651, 13083-859 Campinas, São Paulo, Brazil

  12. An overview of machine learning-enhanced iterative methods for systems of linear and nonlinear equations

    Oct 5, 2026Yuhuang Meng, Jing Zhao, Alexander HeinleinLearning to OptimizeScientific ML

    Delft Institute of Applied Mathematics, Delft University of Technology, Mekelweg 4, Delft, 2628 CD, the Netherlands · Department of Hydrodynamics and Forecasting, Deltares, Boussinesqweg 1, Delft, 2629 HV, the Netherlands

  13. Atoms to Processes: The Role of Artificial Intelligence and Machine Learning in Chemical Engineering

    Oct 1, 2026Michael Baldea, Linda J. Broadbelt, Marianthi G. Ierapetritou +17Scientific MLPhysics-Informed ML

    McKetta Department of Chemical Engineering and Oden Institute for Chemical Engineering and Sciences, The University of Texas at Austin, Austin, TX 78731, USA · Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA · Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA +13

  14. A library for differentiable signal processing and machine learning on the sphere

    Sep 30, 2026Thorsten Kurth, Max Rietmann, Mauro Bisson +6Geometric Representation LearningSpherical Harmonics

    NVIDIA Corporation · California Institute of Technology

  15. A strategic roadmap for an atomistic machine-learning ecosystem

    Sep 30, 2026Jörg Behler, Michele Ceriotti, Cecilia Clementi +47Machine Learning Interatomic PotentialsScientific ML

  16. ALICE: In-context, Zero-shot, Mutual Information Estimation

    Sep 28, 2026Giulio Franzese, Simone Rossi, Pietro MichiardiZero-Shot LearningIn-Context Learning

    EURECOM, Sophia Antipolis, France

  17. The limits of exactness: On the failure of automatic differentiation in physics-informed machine learning

    Sep 27, 2026Ameya D. JagtapAutomatic DifferentiationPDE Surrogate Modeling

    Aerospace Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA

  18. Machine learning for the LHC physics program: a 2025-2026 stocktake

    Sep 26, 2026Jesse ThalerScientific ML

    Center for Theoretical Physics – a Leinweber Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States

  19. ALF: An Active Learning Framework for Scientific Discovery

    Sep 25, 2026Shikha Surana, Alex Hawkins-Hooker, Olivia Gallup +3Active LearningScientific ML

    InstaDeep · University of Oxford

  20. The Mechanics of Delta Learning: Target Design for Generalizable Scientific Machine Learning

    Sep 23, 2026Kareem M. Gameel, Ihor Neporozhnii, Sjoerd Hoogland +1Residual LearningScientific ML

    Department of Physical and Environmental Sciences, University of Toronto Scarborough Alliance for AI-Accelerated Materials Discovery (A3MD) Toronto, Ontario, Canada · Alliance for AI-Accelerated Materials Discovery (A3MD) Toronto, Ontario, Canada · Department of Chemistry, University of Toronto Department of Physical and Environmental Sciences, University of Toronto Scarborough Alliance for AI-Accelerated Materials Discovery (A3MD) Toronto, Ontario, Canada

  21. Targeted Review for AI-Assisted Biodiversity Surveys: Active Continuous-Score Occupancy Modeling

    Sep 22, 2026Timm Haucke, Lauren Harrell, Justin Kay +2Active LearningBiodiversity Monitoring

    MIT · Google Research · The Institute for Bird Populations

  22. An Uncertainty-Aware Hybrid Mathematical-Machine-Learning Model for Smart Irrigation Decision Support

    Sep 12, 2026Andrea ScarioloDecision-Making under UncertaintyPrecision Agriculture

    Independent Researcher

  23. Operational Range Bounding in Spectroscopy: A Safety Cage Framework for Machine Learning Models

    Sep 11, 2026Nikki Grens, Luís F. Simões, Kai Hou Yip +1Scientific MLSelective Prediction

    ML Analytics, 2660-329 Lisbon, Portugal · Department of Space Engineering, Delft University of Technology, 2600 AA Delft, The Netherlands · Department of Physics, King’s College London, University of London, WC2R 2LS, London, United Kingdom +1

  24. Hybrid Physics-AI Framework of Body Center of Mass Dynamics from Wrist-Worn Sensors

    Sep 11, 2026Shuhao Que, Valentina Breschi, Ying WangGait AnalysisWearable Sensing

    Electrical Engineering, University of Twente, Drienerlolaan 5, Enschede, 7522 NB, Netherlands. · Electrical Engineering, Eindhoven University of Technology, Groene Loper 19, Eindhoven, 5612 AP, Netherlands.

  25. SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Science

    Sep 8, 2026Himanshu Patil, Gabby Nyirjesy, Rachel A. Slank +19Benchmark DesignScientific ML

    The University of Alabama in Huntsville, Huntsville, AL, USA · IBM Research, Yorktown Heights, NY, USA · Science and Technology Institute, Universities Space Research Association (USRA), Huntsville, AL, USA +7

  26. SeisBench DAS: A machine learning framework for Distributed Acoustic Sensing

    Sep 7, 2026Jannes Münchmeyer, Han Xiao, Frederik TilmannDistributed Acoustic SensingScientific ML

    GFZ Helmholtz Centre for Geosciences · Institute for Geological Sciences, Freie Universität Berlin, Berlin, Germany

  27. Neural Symbollic Regression Using Deep Learning and Sparse Modelling

    Sep 1, 2026Ravi Kumar U, Sumitra SScientific MLNeurosymbolic Learning

    Department of Mathematics Indian Institute of Space Science and Technology Thiruvananthapuram, India

  28. Traceable Spectral Inference via Influence Functions: Efficient Data Attribution and Error Proxies for the Ariel Mission

    Aug 24, 2026Nikki Grens, Luís F. Simões, Kai Hou Yip +1Influence FunctionsTraining Data Attribution

    ML Analytics, Lisbon, Portugal · Department of Space Engineering, Delft University of Technology, Delft, The Netherlands · Department of Physics, King’s College London, London, UK +1

  29. False-science induction in autonomous scientific discovery

    Aug 20, 2026Hanbing Liang, Fujun LiuAutonomous ExperimentationSpurious Correlation Robustness

    Nanophotonics and Biophotonics Key Laboratory of Jilin Province, School of Physics, Changchun University of Science and Technology, Changchun 130022, P.R. China

  30. ER-KANs: Efficient and Robust Kolmogorov-Arnold Networks for Data-Scarce Scientific Machine Learning

    Aug 14, 2026Harshil LodhiyaKolmogorov-Arnold NetworksNeural Network Robustness

    Sliced Health