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

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

Physics-Embedded Feature Learning for AI in Medical Imaging
Mar 30, 2026Pulock Das, Al Amin, Kamrul Hasan +3Physics-Informed ML Explainable Medical Image Analysis
Tennessee State University, Nashville, TN, USA · Huston–Tillotson University, Austin, TX, USA
Deflation-PINNs: Learning Multiple Solutions for PDEs and Landau-de Gennes
Mar 30, 2026Sean Disarò, Ruma Rani Maity, Aras BachoPDE Solving Physics-Informed ML
Department of Mathematics, Indian Institute of Science, Bangalore, 560012, India · Department of Computing and Mathematical Sciences, California Institute of Technology, CA, USA
Physics-Guided Transformer (PGT): Physics-Aware Attention Mechanism for PINNs
Mar 30, 2026Ehsan Zeraatkar, Rodion Podorozhny, Jelena TešićPhysics-Informed ML Neural PDE Solvers
Computer Science, Texas State University, San Marcos, Texas, US
Transferring Physical Priors into Remote Sensing Segmentation via Large Language Models
Mar 29, 2026Yuxi Lu, Kunqi Li, Zhidong Li +4Multi-Angular Remote Sensing Remote Sensing Image Segmentation
University of Technology Sydney, New South Wales, Australia
Comparing Physics-Informed and Neural ODE Approaches for Modeling Nonlinear Biological Systems: A Case Study Based on the Morris-Lecar Model
Mar 27, 2026Nikolaos M. Matzakos, Chrisovalantis SfyrakisPhysics-Informed ML Neural ODEs
Department of Education, School of Pedagogical and Technological Education (ASPETE), 141 21 Athens, Greece. · Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education (ASPETE), 151 22 Athens, Greece.
Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentation
Mar 27, 2026Imad Ali Shah, Jiarong Li, Ethan Delaney +4Hyperspectral Imaging Physics-Informed ML
School of Engineering, University of Galway, Ireland · Ryan Institute, University of Galway, Ireland · Valeo Vision Systems, Tuam, Ireland
Hyperelastic constitutive model discovery with differentiable finite elements and structure-preserving neural networks
Mar 27, 2026Francesco RegazzoniStructure-Preserving Neural Networks Hyperelasticity
MOX, Department of Mathematics, Politecnico di Milano, Milan, Italy
Physics-Informed Neural Networks and Sequence Encoder: Application to heating and early cooling of thermo-stamping process
Mar 27, 2026Mouad Elaarabi, Domenico Borzacchiello, Philippe Le Bot +2Physics-Informed ML Neural Surrogate Modeling
Nantes Université, Ecole Centrale Nantes, CNRS, GeM, UMR 6183, Nantes, 44321, France · Nantes Université, IRT Jules Verne, Bouguenais, 44340, France
Constitutive parameterized deep energy method for solid mechanics problems with random material parameters
Mar 27, 2026Zhangyong Liang, Jihong Zhu, Huanhuan GaoPhysics-Informed ML Structural Mechanics
National Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China · Key Laboratory of CNC Equipment Reliability, Ministry of Education\National Key Laboratory of Automotive Chassis Integration and Bionics, School of Mechanical and Aerospace Engineering, Jilin University, Renmin Street 5988, 130025, Changchun, China · School of Mechanical and Aerospace Engineering, Jilin University, Renmin Street 5988, 130025, Changchun, China
Pure and physics-guided deep learning approaches for spatio-temporal groundwater level prediction
Mar 26, 2026Matteo Salis, Gabriele Sartor, Rosa Meo +2Physics-Informed ML Attention Mechanisms
Computer Science Department, University of Turin, Turin, Italy · LISTIC Laboratory, Université Savoie Mont Blanc, Annecy-le-vieux, France · Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino and University of Turin, Turin, Italy
Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling
Mar 26, 2026Shiji Zhao, Shukun Xiong, Maoxun Yuan +6Infrared Object Detection Adversarial Robustness
Institute of Artificial Intelligence, Beihang University, No.37 Xueyuan Road, Haidian District, Beijing 100191, P.R. China. · Security Department, Alibaba Group, Hangzhou 310056, China. · School of Computer Science, Nankai University, China. +2
A Comparative Investigation of Thermodynamic Structure-Informed Neural Networks
Mar 26, 2026Guojie Li, Liu HongPhysics-Informed ML PDE Inverse Problems
School of Mathematics, Sun Yat-sen University, Guangzhou, 510275, China.
Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints
Mar 26, 2026Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi +3Interpretable ML Physics-Informed ML
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA · Department of Geosciences, University of Arizona, Tucson, AZ, USA
Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Columns under Transient Operating Conditions
Mar 25, 2026Debadutta Patra, Ayush Bardhan Tripathy, Soumya Ranjan Sahu +1Digital Twins Physics-Informed ML
Department of Chemical Engineering, Veer Surendra Sai University of Technology, Burla 768018, India · Department of Computer Science & Engineering, Veer Surendra Sai University of Technology, Burla 768018, India
LaDy: Lagrangian-Dynamic Informed Network for Skeleton-based Action Segmentation via Spatial-Temporal Modulation
Mar 25, 2026Haoyu Ji, Xueting Liu, Yu Gao +5Temporal Action Segmentation Boundary-Aware Segmentation
Harbin Institute of Technology, Shenzhen · Southern University of Science and Technology · Southeast University
Stochastic Dimension Zeroth-Order Estimator: Stable and Memory-Efficient Training of PINNs
Mar 25, 2026Zhangyong Liang, Huanhuan GaoZeroth-Order Optimization Neural Network Optimization
National Center for Applied Mathematics, Tianjin University, Tianjin 300072, China. · School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China
SafeFlow: Real-Time Text-Driven Humanoid Whole-Body Control via Physics-Guided Rectified Flow and Selective Safety Gating
Mar 25, 2026Hanbyel Cho, Sang-Hun Kim, Jeonguk Kang +1Robotic Control Humanoid Robot Control
Physical AI Lab, RX, Samsung Electronics
Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning
Mar 25, 2026Salah A Faroughi, Farinaz Mostajeran, Amirhossein Arzani +1Interpretable ML Scientific 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
Resolving gradient pathology in physics-informed epidemiological models
Mar 25, 2026Nickson Golooba, Woldegebriel Assefa WoldegerimaGradient Interference Physics-Informed ML
Disease-informed Modeling, Methods & Systems (DIMMS) Lab, Department of Mathematics and Statistics, York University, Toronto, Canada
λSplit: Self-Supervised Content-Aware Spectral Unmixing for Fluorescence Microscopy
Mar 24, 2026Federico Carrara, Talley Lambert, Mehdi Seifi +1Spectral Unmixing Microscopy Image Analysis
Fondazione Human Technopole, Milan, Italy · Università Campus Bio-Medico, Rome, Italy · Harvard Medical School, Boston, MA, USA
SynForceNet: A Force-Driven Global-Local Latent Representation Framework for Lithium-Ion Battery Fault Diagnosis
Mar 24, 2026Rongxiu Chen, Yuting SuFault Diagnosis Online Anomaly Detection
SINO-German College of Intelligent Manufacturing, Shenzhen Technology University, Shenzhen 518118, China
Residual Attention Physics-Informed Neural Networks for Robust Multiphysics Simulation of Steady-State Electrothermal Energy Systems
Mar 24, 2026Yuqing Zhou, Ze Tao, Fujun LiuPhysics-Informed ML Physics-Based Simulation
Nanophotonics and Biophotonics Key Laboratory of Jilin Province, School of Physics, Changchun University of Science and Technology, Changchun, 130022, P.R. China
PhysSkin: Real-Time and Generalizable Physics-Based Animation via Self-Supervised Neural Skinning
Mar 24, 2026Yuanhang Lei, Tao Cheng, Xingxuan Li +6Physics-Based Simulation Physics-Informed ML
State Key Laboratory of CAD&CG, Zhejiang University · BIGAI · Shenzhen University +1
Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
Mar 24, 2026Sebastien Andre-Sloan, Dibyakanti Kumar, Alejandro F Frangi +1Generalization Bounds Physics-Informed ML
Department of Computer Science, The University of Manchester
Double Coupling Architecture and Training Method for Optimization Problems of Differential Algebraic Equations with Parameters
Mar 24, 2026Wenqiang Yang, Wenyuan Wu, Yong Feng +1Constrained Optimization Physics-Informed ML
Chongqing Key Laboratory of Automated Reasoning and Cognition, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Liangjiang District, 400714, Chongqing, China. · Chongqing School, University of Chinese Academy of Sciences, 266 Fangzheng Avenue, Liangjiang District, 400714, Chongqing, China.
Coordinate Encoding on Linear Grids for Physics-Informed Neural Networks
Mar 24, 2026Tetsuro Tsuchino, Motoki ShigaNeural PDE Solvers Physics-Informed ML
Graduate School of Engineering, Gifu University, Japan · International Center for Synchrotron Radiation Innovation Smart, Tohoku University, Japan · Unprecedented-scale Data Analytics Center, Tohoku University, Japan +3
Velocity Potential Neural Field for Efficient Ambisonics Impulse Response Modeling
Mar 23, 2026Yoshiki Masuyama, Francois G. Germain, Gordon Wichern +2Spatial Audio Physics-Informed ML
Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA
Physics-Aware Diffusion for LiDAR Point Cloud Densification
Mar 23, 2026Zeping Zhang, Robert Laganière3D Diffusion Models Physics-Informed ML
VIVA Research Lab, Department of Electrical and Computer Engineering, University of Ottawa, Ottawa, Ontario, Canada
Biophysics-Enhanced Neural Representations for Patient-Specific Respiratory Motion Modeling
Mar 23, 2026Jan Boysen, Hristina Uzunova, Heinz Handels +1Neural Surrogate Modeling Medical Image Analysis
German Research Center for Artificial Intelligence (DFKI), Lübeck, DE · Clinic for Orthopedics and Orthopedic Surgery, University Medicine Greifswald, Greifswald, DE · Institute of Medical Informatics, University of Lübeck, Lübeck, DE
SpecTM: Spectral Targeted Masking for Trustworthy Foundation Models
Mar 23, 2026Syed Usama Imtiaz, Mitra Nasr Azadani, Nasrin AlamdariMasked Image Modeling Geospatial Foundation Models
Department of Civil and Environmental Engineering Florida State University, Tallahassee, FL, USA

Mar 30, 2026J/K move · Enter open · S save

Physics-Embedded Feature Learning for AI in Medical Imaging

Pulock Das, Al Amin, Kamrul Hasan, Rohan Thompson+2

Tennessee State University, Nashville, TN, USA · Huston–Tillotson University, Austin, TX, USA

Physics-Informed ML Explainable Medical Image Analysis Brain Tumor Classification

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Deep learning (DL) models have achieved strong performance in an intelligence healthcare setting, yet most existing approaches operate as black boxes and ignore the physical processes that govern tumor growth, limiting interpretability, robustness, and clinical trust. To address this limitation, we propose PhysNet, a physics-embedded DL framework that integrates tumor growth dynamics directly into the feature learning process of a convolutional neural network (CNN). Unlike conventional physics-informed methods that impose physical constraints only at the output level, PhysNet embeds a reaction diffusion model of tumor growth within intermediate feature representations of a ResNet backbone. The architecture jointly performs multi-class tumor classification while learning a latent tumor density field, its temporal evolution, and biologically meaningful physical parameters, including tumor diffusion and growth rates, through end-to-end training. This design is necessary because purely data-driven models, even when highly accurate or ensemble-based, cannot guarantee physically consistent predictions or provide insight into tumor behavior. Experimental results on a large brain MRI dataset demonstrate that PhysNet outperforms multiple state-of-the-art DL baselines, including MobileNetV2, VGG16, VGG19, and ensemble models, achieving superior classification accuracy and F1-score. In addition to improved performance, PhysNet produces interpretable latent representations and learned bio-physical parameters that align with established medical knowledge, highlighting physics-embedded representation learning as a practical pathway toward more trustworthy and clinically meaningful medical AI systems.