Physics-Informed ML

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60 papers in the last four weeks, up 131% on the four weeks before. 0.6% of all new papers.

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3D Magnetic Field Reconstruction and Mapping with Physics-Informed Neural Networks
May 25, 2026Haohan Yu, Zhanxu Hao, Bingzhi Li +3Physics-Informed ML Inverse Problems
State Key Laboratory of Dark Matter Physics, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China · Key Laboratory for Particle Astrophysics and Cosmology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China · Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University, Shanghai, China +1
MARVEL: Universal Murray's Law-informed Vessel Tree Segmentation and Topology Estimation
May 25, 2026Yi Zhou, Thiara Sana Ahmed, Jacqueline Chua +7Retinal Vessel Segmentation Medical Image Analysis
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore · Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore · Centre for Innovation & Precision Eye Health, Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore +12
PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
May 25, 2026Divyam Goel, Nithin Chalapathi, Sanjeev Raja +1Neural Network Optimization Synthetic Benchmark Generation
Department of Computer Science, UC Berkeley · Departments of Computer Science and Chemical EngineeringMay UC Berkeley; LBNL
DA-UCT: Self-Supervised Domain-Adaptive Ultrasound Computed Tomography for Rapid Musculoskeletal Sound Speed Reconstruction
May 24, 2026Tianyu Liu, Heyu Ma, Aiduo Wang +6Synthetic-to-Real Domain Adaptation Unsupervised Domain Adaptation
College of Biomedical Engineering, Fudan University, Shanghai 200433, China · College of Future Information Technology, Fudan University, Shanghai 200433, China · State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 201203, China
Mitigating Gradient Pathology in PINNs through Aligned Constraint
May 24, 2026Yichen Luo, Peiyu Zhu, Dongxiao Hu +5Neural PDE Solvers Gradient Interference
Department of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden · School of Advanced Manufacturing and Robotics, Peking University, Beijing, China · School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China +2
DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation
May 24, 2026Tianyi Wang, Tianyi Zeng, Zimo Zeng +8Bayesian Neural Networks Physics-Informed ML
Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, Austin, TX 78712, USA. · School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China. · College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China. +3
Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization
May 23, 2026Swapna Pillai, Siddharth Singh Savner, Sujit Kumar SahooSelf-Supervised Image Denoising Self-Supervised Learning
School of Electrical Sciences, Indian Institute of Technology Goa, Goa 403401, India · Inria, Sophia Antipolis, France
SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling
May 23, 2026Pouria Behnoudfar, Deekshith Naidu Ponnana, Noah J. Schmelzer +6Physics-Informed ML Computational Materials Science
Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA · Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, USA · Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, NM, USA +2
Fourier Feature Pyramids for Physics-Informed Neural Networks
May 22, 2026Brandon Zhao, Yixuan Wang, Jonathan T. Barron +3Fourier Feature Embeddings PDE Solving
Department of Computing and Mathematical Sciences, California Institute of Technology · Google DeepMind · Cahill Center for Astronomy and Astrophysics, California Institute of Technology
Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference
May 22, 2026Tewodros Syum Gebre, Jagrati Talreja, Matilda Anokye +1Remote Sensing Image Segmentation Uncertainty Quantification
Built Environment Department, College of Science and Technology, North Carolina A&T State University, Greensboro, NC 27405 USA · United Nations University Institute for Water, Environment and Health, Richmond Hill, ON, Canada
LLM-driven design of physics-constrained constitutive models: two agents are better than one
May 22, 2026Marius Tacke, Matthias Busch, Kian Abdolazizi +4Neural Surrogate Modeling Multi-Agent LLM Systems
1Helmholtz-Zentrum Hereon, Geesthacht, Germany · 2Hamburg University of Technology, Hamburg, Germany · 3RWTH Aachen University, Aachen, Germany +2
Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data
May 22, 2026Matthias Knipper, Chenyi Ji, Malte Brand +1Automatic Differentiation Hyperelasticity
Computational Mechanics in Medicine, Applied Medical Engineering, RWTH Aachen University Pauwelsstraße 20, 52074 Aachen, Germany · Institute for Continuum and Material Mechanics, Hamburg University of Technology, Eißendorfer Straße 42, 21073 Hamburg, Germany
Learning partially observed systems with neural Hamiltonian ordinary differential equations
May 22, 2026Sunniva Meltzer, Sølve Eidnes, Alexander Johannes StasikDynamical Systems Latent Dynamics Modeling
Department of Mathematics and Cybernetics, SINTEF Digital, Norway · Department of Physics, University of Oslo, Norway · Department of Data Science, Norwegian University of Life Science, Norway
Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
May 22, 2026Youngjae Park, Jaemin Kim, Junghwa HongGauss-Newton Optimization Neural Network Optimization
Dept. of Control and Instrumentation Engineering, Korea University, Sejong, South Korea · BK21 FOUR Smart Mobility Education and Research Team, Korea University, Sejong, South Korea
SpinFlow: A Physics-Informed Spin Field Framework for Traffic Phase Inference and Transition Detection
May 22, 2026Haopeng Deng, Fucheng Zheng, Xinhai XiaIntelligent Transportation Systems Traffic Flow Estimation
School of Future Transportation, Guangzhou Maritime University, Guangzhou, China
EMMA: Extracting Multiple physical parameters from Multimodal Data
May 21, 2026Farhat Shaikh, Ayan Banerjee, Sandeep GuptaParameter Estimation Latent Dynamics Modeling
IMPACT Lab, School of Computing & Augmented Intelligence (SCAI) Arizona State University, Tempe, AZ
The Neural Compiler: Program-to-Network Translation for Hybrid Scientific Machine Learning
May 21, 2026Lucas ShenemanDifferentiable Programming Scientific ML
Institute for Interdisciplinary Data Sciences University of Idaho
Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction
May 21, 2026Ziyuan Zhu, Keyu Hu, Zhifei Chen +10Physics-Informed Generative Modeling Physics-Informed ML
School of Advanced Manufacturing Engineering, Nanjing University, Suzhou, 215163, China. · National Laboratory of Solid State Microstructures, Nanjing University, Nanjing, 210093, China. · Suzhou Acoustics Industry Technology Research Institute Co., Ltd., Suzhou, 215513, China. +8
Aerodynamic force reconstruction using physics-informed Gaussian processes
May 21, 2026Gledson Rodrigo Tondo, Igor Kavrakov, Guido MorgenthalPhysics-Informed ML Inverse Problems
Bauhaus-Universität Weimar Weimar, Germany · University of Cambridge Cambridge, United Kingdom
Dual-Integrated Low-Latency Single-Lens Infrared Computational Imaging for Object Detection
May 21, 2026Xuquan Wang, Guishuo Yang, Dapeng Yan +5Infrared Object Detection Computational Imaging
MOE Key Laboratory of Advanced Micro-Structured Materials, the Institute of Precision Optical Engineering, School of Physics Science and Engineering, Tongji University, and the Shanghai Frontiers Science Center of Digital Optics, Shanghai 200092, China · School of Computer Science and Artificial Intelligence, Shandong Normal University, Jinan 250358, China, and the Shandong Engineering Research Center for Multimodal Computing and Intelligent Decision Making, Shandong Normal University, Jinan 250358, China
Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review
May 21, 2026Joseph NyangonPhysics-Informed ML Physics-Informed Neural Operators
Energy Exemplar, Salt Lake City, UT, 84111, USA
Data-Efficient Neural Operator Training via Physics-Based Active Learning
May 20, 2026Alicja Polanska, Lorenzo Zanisi, Vignesh Gopakumar +1Active Learning Physics-Informed ML
University College London · United Kingdom Atomic Energy Authority
Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data
May 20, 2026Islam Mansour, Ronny Haensch, Irena Hajnsek +1Remote Sensing Physics-Informed ML
Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Weßling, Germany · Chair of Earth Observation and Remote Sensing, Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment
May 20, 2026Haozhe Jia, Pengyu Yin, Wenshuo Chen +6Physics-Informed Diffusion Models Physics-Informed Generative Modeling
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Shandong University, Jinan, China · Data61/CSIRO, Australia +5
Fast Reconstruction of Exact Maxwell Dynamics from Sparse Data
May 19, 2026Dan DeGenaro, Xin Li, Obed Amo +4Neural PDE Solvers Neural Network Approximation Theory
Department of Computer Science, Georgetown University · Department of Mathematics, Georgetown University · Department of Mathematics and Statistics, Old Dominion University +2
EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis
May 19, 2026Alzahra Altalib, Chunhui Li, Haytham Ahmad Alewaidat +3Physics-Informed Diffusion Models Cone-Beam CT
School of Science and Engineering, University of Dundee UK · Faculty of Applied Medical Sciences, Jordan University of Science and Technology · Experia Healthcare, Jordan +1
StruMPL: Multi-task Dense Regression under Disjoint Partial Supervision and MNAR Labels
May 19, 2026Reza M. Asiyabi, Juan Alberto Molina-Valero, The SEOSAW Partnership +2Multi-Task Learning Remote Sensing
School of Geosciences, University of Edinburgh, UK · National Centre for Earth Observation (NCEO), UK · Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Praha, Czech Republic
A Closed-loop, State-centric, Multi-agent Framework for Passenger Load Estimation from Heterogeneous Data Streams
May 19, 2026Yiyao Xu, Hao Zhou, Yuhang Wang +1Intelligent Transportation Systems Physics-Informed ML
Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA
Physics Guided Conditional Diffusion Framework for Generative Inverse Design of Manufacturable Metasurface based Absorbers
May 19, 2026Vineetha Joy, Jamshed Palai, Satwik Sahu +3Physics-Informed Generative Modeling Physics-Informed ML
Centre for Electromagnetics, CSIR-National Aerospace Laboratories, Kodihalli, Bangalore 560017 · Birla Institute of Technology and Science, Pilani, Rajasthan 333031 · Indian Institute of Technology, Bombay, Maharashtra 400076
Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments
May 19, 2026Takshak Shende, Viktor PopovPDE Surrogate Modeling Physics-Informed ML
Department of Mechanical Engineering, University College London (UCL), London, United Kingdom · Ascend Technologies Ltd, Southampton, United Kingdom

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3D Magnetic Field Reconstruction and Mapping with Physics-Informed Neural Networks

Haohan Yu, Zhanxu Hao, Bingzhi Li, Zejia Lu+2

State Key Laboratory of Dark Matter Physics, School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai, China · Key Laboratory for Particle Astrophysics and Cosmology (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China · Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University, Shanghai, China+1

Physics-Informed ML Inverse Problems

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Accurate reconstruction of magnetic fields in inaccessible regions is vital for many high-precision experiments in physics. Traditional methods, such as spherical harmonic expansion, often suffer from truncation errors that limit their precision. This study proposes an advanced Physics-Informed Neural Network (PINN) framework for high-precision 3D magnetic field mapping. Unlike conventional data-driven models, the proposed PINN integrates Maxwell's equations directly into the loss function, enforcing divergence-free and curl-free conditions across the entire domain. A key innovation is the inclusion of explicit physics-residual losses at measurement locations, ensuring rigorous physical consistency beyond random collocation sampling. Validation using simulated data achieves a reconstruction accuracy of

10^{-4}

, a tenfold improvement over existing PINN benchmarks. Furthermore, experimental validation using a custom coil assembly demonstrates robust reconstruction with sub-percent relative accuracy, reaching the

10^{-3}

level under ambient conditions. This AI-driven methodology provides a robust, high-precision solution for field monitoring and measurement in complex experimental environments where direct sensor placement is restricted.

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Latest papers 561