Physics-Informed ML

ML: Machine Learning

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

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

Learning to Test: Latent Representation Learning for Dynamic Instability Detection
Apr 13, 2026Minxing Zheng, Zewei Deng, Liyan Xie +1Dynamical Systems Physics-Informed ML
Carnegie Mellon University · University of Minnesota
Battery health prognosis using Physics-informed neural network with Quantum Feature mapping
Apr 11, 2026Muhammad Imran Hossain, Md Fazley Rafy, Sarika Khushalani Solanki +1Lithium-Ion Batteries Physics-Informed ML
Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA, 26505.
A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems
Apr 10, 2026Maryam Ahang, Todd Charter, Masoud Jalayer +1Fault Diagnosis Physics-Informed ML
University of Victoria, Victoria, Canada · Aalto University, Espoo, Finland
Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification
Apr 10, 2026Prasad Nimantha Madusanka Ukwatta Hewage, Midhun Chakkravarthy, Ruvan Kumara AbeysekaraPhysics-Informed ML Hybrid Quantum-Classical ML
Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Selangor, Malaysia · BCAS Campus, Colombo, Sri Lanka
Meta-Learned Basis Adaptation for Parametric Linear PDEs
Apr 10, 2026Vikas Dwivedi, Monica Sigovan, Bruno SixouPDE Solving PDE Surrogate Modeling
CREATIS Biomedical Imaging Laboratory INSA, CNRS UMR 5220, Inserm, Université Lyon 1 Lyon 69621, France
Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data
Apr 10, 2026Stephen Smith, Cormac Purcell, Gabriele Meoni +2Anomaly Detection Physics-Informed ML
School of Physics, University of Sydney, Sydney, Australia · BolgianTen Ltd., Liverpool, United Kingdom · School of Computer Science, University of New South Wales, Sydney, Australia +3
From Phenomenological Fitting to Endogenous Deduction: A Paradigm Leap via Meta-Principle Physics Architecture
Apr 9, 2026Helong Hu, HongDan Pan, ShuiQing HuPhysics-Informed ML Physical Reasoning
Guangdong Ocean University, No.1 Luqin Road, Yangjiang City, Guangdong Province, China（524088） · China Pacific Insurance (Group) Co., Ltd. Foshan Branch, No.17, Jihua 5th Road, Chancheng District, Foshan City, Guangdong Province, China (528000)
Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
Apr 9, 2026Jingye Tan, Govinda Anantha Padmanabha, Steven J. Yang +1Physics-Informed ML Model Calibration
University of Southern California · Cornell University · Ecole Polytechnique Federale de Lausanne (EPFL) +1
Physics-Informed Functional Link Constrained Framework with Domain Mapping for Solving Bending Analysis of an Exponentially Loaded Perforated Beam
Apr 8, 2026Iswari Sahu, Ramanath Garai, S. ChakravertyPhysics-Informed ML Structural Mechanics
Department of Mathematics, National Institute of Technology Rourkela, India
DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting
Apr 8, 2026Yeran Zhang, Pengwei Yang, Guoqing Wang +1Physics-Informed Forecasting Time Series Forecasting
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China · Research Center, East Hope Group Co., Ltd, Shanghai, China
PD-SOVNet: A Physics-Driven Second-Order Vibration Operator Network for Estimating Wheel Polygonal Roughness from Axle-Box Vibrations
Apr 8, 2026Xiancheng Wang, Lin Wang, Rui Wang +5Structural Health Monitoring Physics-Informed ML
School of Ocean Engineering, Harbin Institute of Technology, West Wenhua Road, Weihai, 264209, Shandong, China · Technical Center, Bogie Development Department, CRRC Qingdao Sifang Locomotive and Rolling Stock Co., Ltd, Jinhong East Road, Qingdao, 266111, Shandong, China
Physics-Informed Neural Optimal Control for Precision Immobilization Technique in Emergency Scenarios
Apr 7, 2026Yangye Jiang, Jiachen Wang, Daofei LiOptimal Control Autonomous Driving Planning
Institute of Power Machinery and Vehicular Engineering, Zhejiang University and the Zhejiang Key Laboratory of Intelligent Vehicle Comprehensive Safety, Hangzhou 310027, China
Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies
Apr 7, 2026Prashant Kumar, Rajesh RanjanDomain Decomposition Methods Neural PDE Solvers
Department of Aerospace Engineering, Indian Institute of Technology Kanpur, Kanpur, India 208016
Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks
Apr 6, 2026Anas Jnini, Elham Kiyani, Khemraj Shukla +5Physics-Informed ML Neural PDE Solvers
Department of Information Engineering and Computer Science, University of Trento, Trento, Italy · Division of Applied Mathematics, Brown University, Providence, RI, USA · Departament de Física, Universitat d’Alacant, Spain +3
Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks
Apr 6, 2026Manuel Ballester, Santiago Lopez-Tapia, Seth Gossage +9Astrophysics Neural PDE Solvers
SkAI Institute (NSF–Simons AI Institute for the Sky), Chicago, IL, USA · Department of Electrical and Computer Engineering, Northwestern University, Chicago, IL, USA · CIERA, Northwestern University, Chicago, IL, USA +4
PINNs in PDE Constrained Optimal Control Problems: Direct vs Indirect Methods
Apr 6, 2026Zhen Zhang, Shanqing Liu, Alessandro Alla +2Optimal Control Physics-Informed ML
Division of Applied Mathematics, Brown University · Department of Mathematics at Sapienza, Università di Roma
A physics-informed, behavior-aware digital twin for robust multimodal forecasting of core body temperature in precision livestock farming
Apr 5, 2026Riasad Alvi, Mohaimenul Azam Khan Raiaan, Sadia Sultana Chowa +6Precision Livestock Farming Time Series Forecasting
Applied Artificial INtelligence and Intelligent Systems (AAIINS) Laboratory, Dhaka 1217, Bangladesh · Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh · Department of Data Science and Artificial Intelligence, Monash University, Melbourne 3800, Australia +5
A Theory-guided Weighted $L^2$ Loss for solving the BGK model via Physics-informed neural networks
Apr 4, 2026Gyounghun Ko, Sung-Jun Son, Seung Yeon Cho +1Neural PDE Solvers Physics-Informed ML
Physics-Informed Untrained Learning for RGB-Guided Superresolution Single-Pixel Hyperspectral Imaging
Apr 4, 2026Hao Zhang, Bilige Xu, Lichen Wei +2Hyperspectral Image Reconstruction Hyperspectral Imaging
College of Science, Northwest Agriculture & Forestry University, Yangling 712100, China · Key Laboratory of Photoelectronic Imaging Technology and System, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China · State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks
Apr 3, 2026Solon Falas, Markos Asprou, Charalambos Konstantinou +1False Data Injection Attacks Adversarial Robustness
KIOS Center of Excellence, University of Cyprus · CEMSE, King Abdullah University of Science and Technology (KAUST) · Department of Electrical and Computer Engineering, University of Cyprus
A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
Apr 3, 2026Jeesuk Shin, Donggyun Seo, Sihyeong Yu +1Physics-Based Simulation PDE Surrogate Modeling
Division of Advanced Nuclear Engineering, Pohang University of Science and Technology, Pohang-si, Republic of Korea · Mueunjae School of Undergraduate Studies, Pohang University of Science and Technology, Pohang-si, Republic of Korea · Department of Quantum System Engineering, Jeonbuk National University, Jeonju-si, Republic of Korea
WGFINNs: Weak formulation-based GENERIC formalism informed neural networks
Apr 3, 2026Jun Sur Richard Park, Auroni Huque Hashim, Siu Wun Cheung +2Physics-Informed ML AI-Assisted Scientific Research
Division of Big Data Science, Korea University, Sejong, 30019, Republic of Korea · Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA · Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
Physics-Informed Transformer for Multi-Band Channel Frequency Response Reconstruction
Apr 2, 2026Anatolij Zubow, Joana Angjo, Sigrid Dimce +1Channel Estimation Transformer
School of Electrical Engineering and Computer Science, TU Berlin, Berlin, Germany
General Explicit Network (GEN): A novel deep learning architecture for solving partial differential equations
Apr 2, 2026Genwei Ma, Ting Luo, Ping Yang +1Neural PDE Solvers PDE Solving
National Center for Applied Mathematics Beijing, Capital Normal University, No. 105 West Third Ring Road North, Beijing, 100048, China · The Academy for Multidisciplinary Studies, Capital Normal University, No. 105 West Third Ring Road North, Beijing, 100048, China · Academy of Information Network Security, People’s Public Security University of China, No.1 Muxidi Nanli, Beijing, 100038, China +2
EgoFlow: Gradient-Guided Flow Matching for Egocentric 6DoF Object Motion Generation
Apr 1, 2026Abhishek Saroha, Huajian Zeng, Xingxing Zuo +2Egocentric Video Understanding Flow-Based Generative Modeling
TU München · MCML · MBZUAI +1
Macroscopic transport patterns of UAV traffic in 3D anisotropic wind fields: A constraint-preserving hybrid PINN-FVM approach
Apr 1, 2026Hanbing Liang, Fujun LiuPDE Solving Physics-Informed ML
NeuroDDAF: Neural Dynamic Diffusion-Advection Fields with Evidential Fusion for Air Quality Forecasting
Apr 1, 2026Prasanjit Dey, Soumyabrata Dev, Angela Meyer +1Air Quality Prediction Spatiotemporal Forecasting
Stochastic Dimension Implicit Functional Projections for Global Integral Conservation in High-Dimensional PINNs
Mar 31, 2026Zhangyong Liang, Huanhuan Gao, Manyu XiaoNeural PDE Solvers Physics-Informed ML
National Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China · School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks
Mar 31, 2026Anci Lin, Zhiwen Zhang, Wenju ZhaoPhysics-Informed ML Neural Surrogate Modeling
School of Mathematics, Shandong University, Jinan, Shandong, 250100, P.R. China. · Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong SAR, P.R. China. · Materials Innovation Institute for Life Sciences and Energy (MILES), HKU-SIRI, Shenzhen, P.R. China.
Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy
Mar 31, 2026Jawad Chowdhury, Ganesh Narasimha, Jan-Chi Yang +4Active Learning Physics-Informed ML
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37831, USA. · Department of Physics, National Cheng Kung University, Tainan, 70101, Taiwan.

Apr 13, 2026J/K move · Enter open · S save

Learning to Test: Latent Representation Learning for Dynamic Instability Detection

Minxing Zheng, Zewei Deng, Liyan Xie, Shixiang Zhu

Carnegie Mellon University · University of Minnesota

Dynamical Systems Physics-Informed ML OOD Detection

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Stability assessment is central to safety-critical systems governed by differential-algebraic equations (DAEs), whose dynamics are constrained by physical laws and admissibility conditions. These systems operate under stochastically varying environmental inputs, so stability must be reassessed as the context distribution shifts, yet repeated large-scale DAE simulation is prohibitive in high-dimensional or real-time settings. We propose a test-oriented learning framework that tests a trajectory-level chance constraint from deployment contexts alone. Rather than re-estimating physical parameters or re-solving the DAE, we learn a latent representation from baseline context-trajectory data that retains stability-relevant information and is regularized toward a tractable reference distribution, and calibrate a latent discrepancy test over the composite safety null. We establish exact validity for oracle calibration and asymptotic validity for its empirical implementation under class-conditional latent invariance, and show that the guarantee degrades gracefully when this invariance holds only approximately. On simulated dynamical systems and a power-system real-world example, our approach strikes a balance between validity and power, whereas other baseline methods each fail in at least one regime. An ablation that injects controlled violations of the invariance assumption shows that decisions stay correct while the violation is below the safety margin, as the theory predicts.