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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Multifidelity Gaussian process regression for solving nonlinear partial differential equations
May 11, 2026Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier RoustantSurrogate Modeling PDE Solving
Institut de Mathématiques de Toulouse, Université de Toulouse, INSA, Toulouse, 31077, France · CMAP, CNRS, Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, 91120, France
DynGhost: Temporally-Modelled Transformer for Dynamic Ghost Imagings
May 11, 2026Vittorio Palladino, Ahmet Enis CetinVision Transformer Image Reconstruction
Politecnico di Milano Milan, Italy · University of Illinois at Chicago Chicago, IL, USA · Department of Electrical and Computer Engineering University of Illinois at Chicago Chicago, IL, USA +1
Per-Loss Adapters for Gradient Conflict in Physics-Informed Neural Networks
May 11, 2026Bum Jun Kim, Gnankan Landry Regis N'guessanGradient Interference Adaptive Loss Weighting
The University of Tokyo, Japan · Axiom Research Group · Department of Applied Mathematics and Computational Science, NM-AIST, Tanzania +3
Chebyshev Center-Based Direction Selection for Multi-Objective Optimization and Training PINNs
May 11, 2026Hoyeol Yoon, Seoungbin Bae, Nam Ho-Nguyen +1Neural PDE Solvers Neural Network Optimization
Department of Industrial & Systems Engineering, KAIST · Discipline of Business Analytics, The University of Sydney · Department of Mathematical Sciences, Seoul National University
Multi-Tier Labeling and Physics-Informed Learning for Orbital Anomaly Detection at Scale
May 10, 2026Yong FuWeakly Supervised Learning Physics-Informed ML
Substratum Labs, Inc.
Adaptive Data Harvesting for Efficient Neural Network Learning with Universal Constraints
May 10, 2026Siteng Kang, Xinhua ZhangAdaptive Sampling Physics-Informed ML
University of Illinois Chicago
Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators
May 10, 2026S. A. Shteingolts, Salman N. Salman, Dan MendelsGraph Neural Networks OOD Generalization
The Wolfson Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 32000, Israel
Finite Volume-Informed Neural Network Framework for 2D Shallow Water Equations: Rugged Loss Landscapes and the Importance of Data Guidance
May 9, 2026Xiaofeng LiuPDE Solving PDE Surrogate Modeling
Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, USA · Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA
Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials
May 9, 2026Vojtěch Votruba, Zequn He, Weilun Qiu +2Dynamical Systems Nonlinear System Identification
Mathematical Institute, Faculty of Mathematics and Physics, Charles University, Prague, 18675, Czech Republic · Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, 19104, PA, United States of America
Physics-Informed Neural PDE Solvers via Spatio-Temporal MeanFlow
May 9, 2026Hanru Bai, Yuncheng Zhou, Difan ZouFlow Matching Neural PDE Solvers
1Fudan University · 2The University of Hong Kong.
PG-LRF: Physiology-Guided Latent Rectified Flow for Electro-Hemodynamic PPG-to-ECG Generation
May 9, 2026Xiaoda Wang, Minxiao Wang, Kaiqiao Han +10Electrocardiography Physics-Informed ML
Department of Computer Science, Emory University · Department of Computer Science, University of California, Los Angeles · Nell Hodgson Woodruff School of Nursing, Emory University +2
DynSSM: A Physics-Aware State-Space Memory Framework for Learning Vehicle Dynamics
May 8, 2026Musabbir Ahmed Arrafi, Malik Ali, Nicholas M. Stiffler +1Autonomous Racing Vehicle Dynamics
University of Dayton, Dayton, OH 45469 USA
AdamFLIP: Adaptive Momentum Feedback Linearization Optimization for Hard Constrained PINN Training
May 8, 2026Binghang Lu, Runyu Zhang, Changhong Mou +2Neural PDE Solvers Constrained Optimization
Purdue University · Massachusetts Institute of Technology · Utah State University +1
Observation-Aligned Two-Stage Domain Decomposition for Physics-Informed Traffic State Estimation with Sparse Fixed Sensors
May 8, 2026Eunhan Ka, Ludovic Leclercq, Satish V. UkkusuriTraffic Flow Estimation Physics-Informed ML
Lyles School of Civil and Construction Engineering, Purdue University, 610 Purdue Mall, West Lafayette, 47907, IN, United States · Univ Gustave Eiffel, ENTPE, LICIT-ECO7, Lyon, F-69675, France
When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains
May 8, 2026Brandon Yee, Pairie Koh, Jack Rodriguez +1Neural PDE Solvers PDE Solving
Physics Lab, Yee Collins Research Group
Cloud-top infrared observations reveal the four-dimensional precipitation structure
May 8, 2026Tianchi Xu, Ziqiang Ma, Andrea Marinoni +11Remote Sensing Physics-Informed ML
Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, 100871, China · Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom · Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education; School of Computer Science, Peking University, Beijing, 100871, China +4
Excluding the Target Domain Improves Extrapolation: Deconfounded Hierarchical Physics Constraints
May 8, 2026Tsuyoshi OkitaOOD Generalization Physics-Informed ML
Kyushu Institute of Technology
Accelerated and data-efficient flow prediction in stirred tanks via physics-informed learning
May 8, 2026Mahdi Naderibeni, Liang Wu, David M. J. TaxPDE Surrogate Modeling Physics-Informed ML
aPattern Recognition and Bio-informatics Group, Delft University of Technology, Delft, the Netherlands · bScience & Research and Innovation, dsm-firmenich, Delft, the Netherlands
PIMSM: Physics-Informed Multi-Scale Mamba for Stable Neural Representations under Distribution Shift
May 8, 2026Sangyoon Bae, Shinjae Yoo, Jiook ChaDomain Generalization Physics-Informed ML
Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul, South Korea, 08826 · Department of Psychology Seoul National University Seoul, South Korea, 08826
Learned Lagrangian Models of PDEs via Euler-Lagrange Residual Minimization
May 8, 2026Lyra Zhornyak, Eric Forgoston, M. Ani HsiehNeural PDE Solvers PDE Surrogate Modeling
University of Pennsylvania · Montclair State University
Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
May 8, 2026Ryoichiro Agata, Tomohisa OkazakiBayesian Inverse Problems Physics-Informed ML
Japan Agency for Marine-Earth Science and Technology, Japan · Disaster Prevention Research Institute, Kyoto University, Japan · RIKEN Center for Advanced Intelligence Project, Japan
Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices
May 7, 2026Joseph Quaratiello, Anthony RizzoNeural Surrogate Modeling Physics-Informed ML
Thayer School of Engineering, Dartmouth College, Hanover, NH 03755
Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning
May 7, 2026Umme Mahbuba Nabila, Paul Seurin, Linyu Lin +1Surrogate Modeling Sparse Identification of Nonlinear Dynamics
Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 48109, United States · Nuclear Science & Technology Division, Idaho National Laboratory, Idaho Falls, ID 83415, United States · Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, United States
Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations
May 7, 2026Jan-Hendrik Ewering, Kathrin Flaßkamp, Niklas Wahlström +2Latent Dynamics Modeling Physics-Informed ML
Leibniz Universität Hannover · Saarland University · Uppsala University
Region-adaptable retrieval of coastal biogeochemical parameters from near-surface hyperspectral remote sensing reflectance using physics-aware meta-learning
May 7, 2026Yiqing Guo, Nagur R. C. Cherukuru, Eric A. Lehmann +7Meta-Learning Remote Sensing
a · b · c +5
DualTCN: A Physics-Constrained Temporal Convolutional Network for 2 Time-Domain Marine CSEM Inversion
May 6, 2026Khaled Ahmed, Ghada OmarTemporal Convolutional Networks Physics-Informed ML
School of Computing, Southern Illinois University Carbondale, Carbondale, IL, 62901, USA · School of Mathematics and Statistics, Southern Illinois University Carbondale, Carbondale, IL, 62901, USA
Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
May 6, 2026Miloš Babić, Franz M. Rohrhofer, Stefan PoschDynamical Systems Differentiable Physics
CD Laboratory for Physics-driven Machine Learning in Industrial Applications, Graz, Austria · The Institute of Thermodynamics and Sustainable Propulsion Systems, Graz University of Technology, Graz, Austria · Know Center Research GmbH, Graz, Austria
Gradient Scaling Effects in Adaptive Spectral PINNs for Stiff Nonlinear ODEs
May 6, 2026Isabela M. Yepes, Pavlos ProtopapasDynamical Systems Neural Network Optimization
Harvard University
Physics-Guided Regime Unmixing
May 5, 2026Paula Pacheco, Pablo Granitto, Juan B. CabralHyperspectral Imaging Physics-Informed ML
GVT-CONAE, Centro Espacial Teófilo Tabanera, Argentina · CONICET, Argentina · FAMAF-UNC, Córdoba, Argentina +1
Simultaneous CNN Approximation on Manifolds with Applications to Boundary Value Problems
May 5, 2026Hanfei Zhou, Lei ShiPDE Solving Neural Network Approximation Theory
School of Mathematical Sciences, Fudan University, Shanghai, 200433, China · Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai, 200433, China

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Multifidelity Gaussian process regression for solving nonlinear partial differential equations

Fatima-Zahrae El-Boukkouri, Josselin Garnier, Olivier Roustant

Institut de Mathématiques de Toulouse, Université de Toulouse, INSA, Toulouse, 31077, France · CMAP, CNRS, Ecole polytechnique, Institut Polytechnique de Paris, Palaiseau, 91120, France

Surrogate Modeling PDE Solving Multifidelity Modeling Physics-Informed ML Gaussian Process Regression

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Solving nonlinear partial differential equations (PDEs) using kernel methods offers a compelling alternative to traditional numerical solvers. However, the performance of these methods strongly depends on the choice of kernel. In this work, as the available information is inherently multifidelity, we propose a kernel learning approach based on cokriging, leveraging empirical information from multifidelity simulations. In the first step, we fit a differentiable non-stationary kernel to an empirical kernel obtained from low-fidelity simulations. In the second step, we derive a high-fidelity kernel with estimated hyperparameters, and construct a corresponding high-fidelity mean using the multifidelity framework. These components can then be used within a Gaussian process framework for solving PDEs. Finally, we demonstrate the performance of the proposed physics-informed method on the Burgers' equation.

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Latest papers 561