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

  1. A Stability-Aware Frozen Euler Autoencoder for Physics-Informed Tracking in Continuum Mechanics (SAFE-PIT-CM)

    Feb 27, 2026Emil HovadPhysics-Informed MLPDE Inverse Problems

    Section for Statistics and Data Analysis, DTU Compute, Technical University of Denmark, Kgs. Lyngby, Denmark

  2. Physics-informed neural particle flow for the Bayesian update step

    Feb 26, 2026Domonkos Csuzdi, Tamás Bécsi, Olivér TörőParticle FilteringBayesian Filtering

  3. Physics-Informed Machine Learning for Vessel Shaft Power and Fuel Consumption Prediction: Interpretable KAN-based Approach

    Feb 25, 2026Hamza Haruna Mohammed, Dusica Marijan, Arnbjørn MaressaMaritime TransportationPhysics-Informed ML

    Simula Research Laboratory, Oslo, Norway · Navtor AS, Egersund, Norway

  4. Solving stiff dark matter equations via Jacobian Normalization with Physics-Informed Neural Networks

    Feb 25, 2026M. P. Bento, H. B. Câmara, J. R. Rocha +1Physics-Informed MLDynamical Systems

    Departamento de Física and CFTP, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal · Institute of Experimental and Applied Physics, Czech Technical University in Prague, Prague 160 00, Czech Republic

  5. Dual-Regime Hybrid Aerodynamic Modeling of Winged Blimps With Neural Mixing

    Feb 25, 2026Xiaorui Wang, Hongwu Wang, Yue Fan +2Vehicle DynamicsSystem Identification

    The authors are with the Robotics and Control Laboratory, School of Advanced Manufacturing and Robotics, and the State Key Laboratory of Turbulence and Complex Systems, Peking University, Beijing, 100871, China

  6. Physics-constrained symbolic regression for discovering closed-form equations of multimodal water retention curves from experimental data

    Feb 24, 2026Yejin Kim, Hyoung Suk SuhPorous Media FlowSymbolic Regression

    Department of Civil and Environmental Engineering, Case Western Reserve University, United States

  7. Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems

    Feb 23, 2026Yongsheng Chen, Yong Chen, Wei Guo +1Physics-Informed MLPDE Inverse Problems

    School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China. · Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA. · Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 70409, USA.

  8. Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction

    Feb 23, 2026Pao-Hsiung Chiu, Jian Cheng Wong, Chin Chun Ooi +3Neural PDE SolversPhysics-Informed ML

    Institute of High Performance Computing, A*STAR, Singapore. · Centre for Frontier AI Research, A*STAR, Singapore. · School of Mechanical Engineering, Tianjin University, China. +1

  9. PIS: A Physics-Informed System for Accurate State Partitioning of Aβ42Aβ_{42} Protein Trajectories

    Feb 23, 2026Qianfeng Yu, Ningkang Peng, Yanhui GuPhysics-Informed ML

    School of Computer and Electronic Information, Nanjing Normal University

  10. Spectral bias in physics-informed and operator learning: Analysis and mitigation guidelines

    Feb 22, 2026Siavash Khodakarami, Vivek Oommen, Nazanin Ahmadi Daryakenari +2Spectral BiasPhysics-Informed ML

    Division of Applied Mathematics, Brown University, Providence, RI 02912, USA · School of Engineering, Brown University, Providence, RI 02912, USA

  11. Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study

    Feb 20, 2026Orfeas Bourchas, George PapalambrouPhysics-Informed MLResidual Learning

    Laboratory of Marine Engineering N.T.U.A.

  12. Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria

    Feb 20, 2026Jan Pavšek, Alexander Mitsos, Elvis J. Sim +1Molecular Property PredictionMaterials Property Prediction

    Process Systems Engineering (AVT.SVT), RWTH Aachen University · JARA Center for Simulation and Data Science (CSD) · Institute of Climate and Energy Systems ICE-1: Energy Systems Engineering, Forschungszentrum Jülich GmbH

  13. Deepmechanics

    Feb 20, 2026Abhay Shinde, Aryan Amit Barsainyan, Jose Siguenza +3Symplectic RNNsLagrangian Neural Networks

    Deep Forest Sciences, Mumbai, Maharashtra, India · Deep Forest Sciences, Bengaluru, Karnataka, India · Deep Forest Sciences, Palo alto, California, USA +1

  14. PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes

    Feb 20, 2026Karkulali Pugalenthi, Jian Cheng Wong, Qizheng Yang +4Parameter EstimationLithium-Ion Batteries

    Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research, 5 Cleantech Loop, Singapore, 636732, Singapore · Institute of High Performance Computing, Agency for Science, Technology and Research, 1 Fusionopolis Way, Singapore, 138632, Singapore · Department of Mathematics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, China +3

  15. Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors

    Feb 19, 2026Xiangyu Sun, Shirin Hosseinmardi, Amin Yousefpour +1Topology OptimizationPhysics-Informed ML

    Department of Mechanical and Aerospace Engineering, University of California, Irvine · Department of Civil and Environmental Engineering, University of California, Irvine

  16. Solving and learning advective multiscale Darcian dynamics with the Neural Basis Method

    Feb 19, 2026Yuhe Wang, Min WangNeural PDE SolversPhysics-Informed ML

    Institute for Scientific Computation, Texas A&M University, College Station, TX 77843, USA · Department of Mathematics, University of Houston, Houston, TX 77204, USA

  17. QuPAINT: Physics-Aware Instruction Tuning Approach to Quantum Material Discovery

    Feb 19, 2026Xuan-Bac Nguyen, Hoang-Quan Nguyen, Sankalp Pandey +4Microscopy Image AnalysisMultimodal Instruction Tuning

    CVIU Lab, University of Arkansas, USA · University of Utah, USA · Department of Physics, University of Arkansas, USA

  18. TopoFlow: Topography-aware Pollutant Flow Learning for High-Resolution Air Quality Prediction

    Feb 18, 2026Ammar Kheder, Helmi Toropainen, Wenqing Peng +4Air Quality PredictionSpatiotemporal Forecasting

    Department of Computational Engineering, Lappeenranta–Lahti University of Technology LUT, Finland · Atmospheric Modelling Centre Lahti, Lahti University Campus, Finland · Institute for Atmospheric and Earth System Research, University of Helsinki, P.O. Box 64, Helsinki 00014, Finland +2

  19. RSS map-assisted MIMO channel estimation in the upper mid-band under pilot constraints

    Feb 18, 2026Alireza Javid, Nuria González-PrelcicChannel EstimationWireless Communications

    Department of Electrical and Computer Engineering, University of California San Diego, USA

  20. AI-Driven Structure Refinement of X-ray Diffraction

    Feb 18, 2026Bin Cao, Qian Zhang, Zhenjie Feng +4Materials SciencePhysics-Informed ML

    Guangzhou Municipal Key Laboratory of Materials Informatics, Advanced Materials Thrust, Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · Materials Genome Institute, Shanghai University, Shanghai 200444, China · Hefei National Research Center for Physical Sciences at the Microscale, and Hefei National Laboratory, University of Science and Technology of China, Hefei, Anhui 230026, China +1

  21. Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks

    Feb 18, 2026Sarkis Ter Martirosyan, Xinyue Huang, David Qin +2Spectral UnmixingPhotoacoustic Tomography

    School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States · Institute of Biological and Medical Imaging, Helmholtz Zentrum Munich, 85764 Neuherberg, Germany · Chair of Biological Imaging, Technische Universität München, 80333 München, Germany +1

  22. Rethinking Input Domains in Physics-Informed Neural Networks via Geometric Compactification Mappings

    Feb 18, 2026Zhenzhen Huang, Haoyu Bian, Jiaquan Zhang +6Neural PDE SolversPhysics-Informed ML

    School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China · Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China · Institute of Software Chinese Academy of Sciences, Beijing, China +1

  23. Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning

    Feb 18, 2026Binghang Lu, Jiahao Zhang, Guang LinMuon OptimizerPhysics-Informed ML

    Department of Mathematics, Purdue University, West Lafayette, IN 47907, USA · Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA

  24. Imaging-Derived Coronary Fractional Flow Reserve: Advances in Physics-Based, Machine Learning, and Physics-Informed Methods

    Feb 17, 2026Tanxin Zhu, Emran Hossen, Chen Zhao +4Medical Image AnalysisPhysics-Informed ML

    Department of Applied Computing, Michigan Technological University, Houghton, MI, USA · Department of Computer Science, Kennesaw State University, Marietta GA, USA · Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA +3

  25. Hybrid Model Predictive Control with Physics-Informed Neural Network for Satellite Attitude Control

    Feb 17, 2026Carlo Cena, Mauro Martini, Marcello ChiabergeSpacecraft Attitude DeterminationModel Predictive Control

    Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy

  26. Parameter-Minimal Neural DE Solvers via Horner Polynomials

    Feb 16, 2026T. Matulić, D. SeršićNeural PDE SolversPolynomial Neural Networks

    Dept. of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia

  27. Pseudo-differential-enhanced physics-informed neural networks

    Feb 16, 2026Andrew GracykPDE SolvingSpectral Bias

    Department of Mathematics, Purdue University, West Lafayette, IN 47907, United States

  28. A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization

    Feb 14, 2026Suhas Suresh Bharadwaj, Reuben Thomas ThovelilNeural PDE SolversPDE Solving

    Department of Electrical and Electronics Engineering, BITS Pilani, Dubai Campus, Dubai, United Arab Emirates

  29. Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training

    Feb 10, 2026Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin +2Physics-Informed Generative ModelingPDE Surrogate Modeling

    Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu, Taiwan · Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu, Taiwan · Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu, Taiwan National Center for Theoretical Sciences National Taiwan University Taipei, Taiwan

  30. When Does Cheap Physics Help? Gated Multi-Fidelity Physics-Informed Networks with Provable Sample Savings and Conformal Guarantees

    Feb 1, 2026Olaf Yunus Laitinen Imanov, Rana Irem Turhan, Isgandar JafarliMultifidelity ModelingPhysics-Informed ML

    Department of Mathematics and Statistics, University of Helsinki, Finland · School of Computer Science, University of Sheffield, United Kingdom · School of IT and Engineering, ADA University, Azerbaijan