Physics-Informed ML

ML: Machine Learning

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Overview

Physics-informed machine learning studies how to combine observations with governing equations, conservation laws, symmetries, and other scientific knowledge. Common approaches include physics-informed neural networks (PINNs) that penalize equation residuals, weak or variational formulations, neural operators for families of PDE solutions, and hybrid models that learn corrections to mechanistic simulators. These methods are used for forward simulation, inverse problems, parameter estimation, forecasting, and fast surrogates when measurements or computational resources are limited. A central challenge is that physical constraints can improve data efficiency and consistency but can also hinder training or harm predictions when the equations, discretization, or assumed structure do not match the system.

Latest papers

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844 papers75 papers in the last four weeks, down 13% on the four weeks before. 0.4% of all new papers.

  1. A Novel Method for Enforcing Exactly Dirichlet, Neumann and Robin Conditions on Curved Domain Boundaries for Physics Informed Machine Learning

    Mar 23, 2026Suchuan Dong, Yuchuan ZhangPDE SolvingPhysics-Informed ML

    Center for Computational and Applied Mathematics Department of Mathematics Purdue University, USA

  2. Stability and Bifurcation Analysis of Nonlinear PDEs via Random Projection-based PINNs: A Krylov-Arnoldi Approach

    Mar 23, 2026Gianluca Fabiani, Michail E. Kavousanakis, Constantinos Siettos +1Dynamical SystemsNeural PDE Solvers

    Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, 21218, MD, USA · Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA · School of Chemical Engineering, National Technical University of Athens, 9 Iroon Polytechniou Street, Athens, 15772, Greece +2

  3. Benchmarking Scientific Machine Learning Models for Air Quality Data

    Mar 22, 2026Khawja Imran Masud, Venkata Sai Rahul Unnam, Sahara AliAir Quality PredictionForecasting Benchmarks

    University of North Texas, 76205 Texas, USA

  4. Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers

    Mar 21, 2026Giorgio Morales, Gregory Lehaut, Antonin Vacheret +2Parameter EstimationHigh-Energy Physics

    Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC. Caen, France · Normandie Univ, ENSICAEN, UNICAEN, CNRS/IN2P3, LPC Caen. Caen, France

  5. Interpretable Battery Aging without Extra Tests via Neural-Assisted Physics-based Modelling

    Mar 21, 2026Yuan Qiu, Wei Li, Wei Zhang +4Lithium-Ion BatteriesPhysics-Informed ML

    Singapore Institute of Technology (SIT), Singapore, 828608 · Singapore University of Social Sciences (SUSS), Singapore, 599494 · Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore

  6. PhyUnfold-Net: Advancing Remote Sensing Change Detection with Physics-Guided Deep Unfolding

    Mar 20, 2026Zelin Lei, Yaoxing Ren, Jiaming ChangRemote Sensing Change DetectionPhysics-Informed ML

    Xi’an Jiaotong University · Anhui University

  7. An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media

    Mar 20, 2026V. S. Maduri, K. B. NakshatralaNeural PDE SolversPorous Media Flow

    Department of Civil & Environmental Engineering University of Houston, Houston, Texas 77204, USA.

  8. Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations

    Mar 20, 2026Jun LiuHJB EquationsNeural PDE Solvers

    Department of Applied Mathematics, Faculty of Mathematics, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada

  9. Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees

    Mar 19, 2026Amartya Mukherjee, Maxwell Fitzsimmons, David C. Del Rey Fernández +1Neural Network GeneralizationNeural PDE Solvers

    Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada

  10. WarPGNN: A Parametric Thermal Warpage Analysis Framework with Physics-aware Graph Neural Network

    Mar 19, 2026Haotian Lu, Jincong Lu, Sachin Sachdeva +1Graph Neural NetworksPhysics-Informed ML

    Department of Electrical and Computer Engineering · University of California, Riverside · Riverside, CA, USA

  11. A Family of Adaptive Activation Functions for Mitigating Failure Modes in Physics-Informed Neural Networks

    Mar 18, 2026Krishna MurariNeural Network Activation FunctionsPhysics-Informed ML

    †Department of Mathematics, IIT Madras, Chennai 600036, India

  12. Physics-Aware Machine Learning for Seismic and Volcanic Signal Interpretation

    Mar 18, 2026William ThorossianPhysics-Informed MLSeismology

  13. Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation

    Mar 18, 2026Petros Ellinas, Indrajit Chaudhuri, Johanna Vorwerk +1Surrogate ModelingPower Systems

    Department of Wind and Energy Systems, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark.

  14. Symmetry-Reduced Physics-Informed Learning of Tensegrity Dynamics

    Mar 18, 2026Jing Qin, Muhao ChenPhysics-Informed MLStructural Mechanics

    Department of Mathematics, University of Kentucky, Lexington, KY · Department of Mechanical and Aerospace Engineering, University of Houston, TX

  15. Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer

    Mar 18, 2026Mohammad NooraiepourMaterials Property PredictionPorous Media Flow

    Faculty of Mathematics and Natural Sciences, University of Oslo, PO Box 1047 Blindern, 0316 Oslo, Norway

  16. AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecasting

    Mar 18, 2026Binqing Wu, Zongjiang Shang, Shiyu Liu +3Air Quality PredictionMultivariate Time Series Forecasting

    State Key Laboratory of Blockchain and Data Security, Zhejiang University · College of Computer Science and Technology, Zhejiang University · State Key Laboratory of Clean Energy Utilization, Zhejiang University

  17. Physics-informed Deep Mixture-of-Koopmans Vehicle Dynamics Model with Dual-branch Encoder for Distributed Electric-drive Trucks

    Mar 18, 2026Jinyu Miao, Pu Zhang, Rujun Yan +8Vehicle DynamicsKoopman Operator Learning

    School of Vehicle and Mobility, and State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China · KargoBot Inc., Beijing, China

  18. Domain-informed explainable boosting machines for trustworthy lateral spread predictions

    Mar 17, 2026Cheng-Hsi Hsiao, Krishna Kumar, Ellen M. RathjeInterpretable MLPhysics-Informed ML

  19. pADAM: A Plug-and-Play All-in-One Diffusion Architecture for Multi-Physics Learning

    Mar 17, 2026Amirhossein Mollaali, Bongseok Kim, Christian Moya +1Physics-Informed Generative ModelingPDE Surrogate Modeling

    School of Mechanical Engineering, Purdue University, West Lafayette, 47906, IN, USA. · Department of Mathematics, Purdue University, West Lafayette, 47906, IN, USA.

  20. Minimum-Action Learning: Energy-Constrained Symbolic Model Selection for Physical Law Identification from Noisy Data

    Mar 16, 2026Martin G. FraschScientific DiscoveryModel Selection

    Institute on Human Development and Disability, University of Washington, Seattle, WA 98195, USA · Health Stream Analytics, LLC, Seattle, WA, USA

  21. OMNIFLOW: A Physics-Grounded Multimodal Agent for Generalized Scientific Reasoning

    Mar 16, 2026Hao Wu, Yongheng Zhang, Yuan Gao +7Scientific Reasoning in Language ModelsPhysics-Informed ML

    Tsinghua University · Tencent · Shenzhen Loop Area Institute +2

  22. Physics-Informed Neural Systems for the Simulation of EUV Electromagnetic Wave Diffraction from a Lithography Mask

    Mar 16, 2026Vasiliy A. Es'kin, Egor V. IvanovNeural PDE SolversNeural Operators

    Department of Radiophysics, University of Nizhny Novgorod 23 Gagarin Ave., Nizhny Novgorod 603022, Russia

  23. Building Trust in PINNs: Error Estimation through Finite Difference Methods

    Mar 16, 2026Aleksander Krasowski, René P. Klausen, Aycan Celik +3Physics-Informed MLPDE Solving

    Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute · Centre of eXplainable Artificial Intelligence, Technological University Dublin · Department of Electrical Engineering and Computer Science, Technische Universität Berlin +1

  24. Physics-informed fine-tuning of foundation models for partial differential equations

    Mar 16, 2026Vlad Medvedev, Leon Armbruster, Christopher Straub +2PDE Foundation ModelsPDE Surrogate Modeling

    Department Modeling and Artificial Intelligence Fraunhofer Institute for Integrated Systems and Device Technology IISB Schottkystrasse 10, 91058 Erlangen, Germany

  25. A scaled TW-PINN: A physics-informed neural network for traveling wave solutions of reaction-diffusion equations with general coefficients

    Mar 16, 2026Seungwan Han, Kwanghyuk Park, Jiaxi Gu +1Reaction-Diffusion SystemsPDE Solving

  26. 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

  27. PASTE: Physics-Aware Scattering Topology Embedding Framework for SAR Object Detection

    Mar 16, 2026Jiacheng Chen, Yuxuan Xiong, Haipeng WangObject DetectionSynthetic Aperture Radar

    Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China

  28. Predicting Stress-strain Behaviors of Additively Manufactured Materials via Loss-based and Activation-based Physics-informed Machine Learning

    Mar 15, 2026Chenglong Duan, Dazhong WuMaterials Property PredictionPhysics-Informed ML

    Department of Mechanical and Aerospace Engineering, College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA

  29. Manifold-Orthogonal Dual-spectrum Extrapolation for Parameterized Physics-Informed Neural Networks

    Mar 14, 2026Zhangyong Liang, Huanhuan GaoPDE Operator LearningPhysics-Informed ML

  30. Assessing the Utility of Volumetric Motion Fields for Radar-based Precipitation Nowcasting with Physics-informed Deep Learning

    Mar 13, 2026Peter Pavlík, Anna Bou Ezzeddine, Viera RozinajováPrecipitation NowcastingPrecipitation Forecasting

    Kempelen Institute of Intelligent Technologies, Sky Park Offices, Bottova 7939/2A, Bratislava 811 09, Slovakia · Faculty of Information Technology, Brno University of Technology, Božetěchova 2, Brno 612 00, Czech Republic