Physics-Informed ML

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93 papers in the last four weeks, up 26% on the four weeks before. 0.6% of all new papers.

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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
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
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
Stability and Bifurcation Analysis of Nonlinear PDEs via Random Projection-based PINNs: A Krylov-Arnoldi Approach
Mar 23, 2026Gianluca Fabiani, Michail E. Kavousanakis, Constantinos Siettos +1Dynamical Systems Neural PDE Solvers
Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, 21218, MD, USA · Dept. of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA · School of Chemical Engineering, National Technical University of Athens, 9 Iroon Polytechniou Street, Athens, 15772, Greece +2
An Adaptive Machine Learning Framework for Fluid Flow in Dual-Network Porous Media
Mar 20, 2026V. S. Maduri, K. B. NakshatralaNeural PDE Solvers Physics-Informed ML
Department of Civil & Environmental Engineering University of Houston, Houston, Texas 77204, USA.
Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees
Mar 19, 2026Amartya Mukherjee, Maxwell Fitzsimmons, David C. Del Rey Fernández +1Neural Network Generalization Neural PDE Solvers
Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada
WarPGNN: A Parametric Thermal Warpage Analysis Framework with Physics-aware Graph Neural Network
Mar 19, 2026Haotian Lu, Jincong Lu, Sachin Sachdeva +1Graph Neural Networks Physics-Informed ML
Department of Electrical and Computer Engineering · University of California, Riverside · Riverside, CA, USA
Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
Mar 13, 2026Zhangyong Liang, Huanhuan GaoLatent Dynamics Modeling PDE Solving
National Center for Applied Mathematics, Tianjin University, Tianjin, 300072, PR China · School of Mechanical and Aerospace Engineering, Jilin University, Changchun, 130025, China
Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification
Mar 10, 2026Junhyeok Lee, Minseo Choi, Han Jang +5Evidential Deep Learning Uncertainty Quantification
Cancer Biology, Seoul National University College of Medicine, Seoul, Korea · Radiology, Seoul National University Hospital, Seoul, Korea · Bioengineering, Seoul National University, Seoul, Korea +3
MUSA-PINN: Multi-scale Weak-form Physics-Informed Neural Networks for Fluid Flow in Complex Geometries
Mar 9, 2026Weizheng Zhang, Xunjie Xie, Hao Pan +4Neural PDE Solvers PDE Solving
Shandong University, Qingdao, China · Tsinghua University, Beijing, China · Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, China
Joint 3D Gravity and Magnetic Inversion via Rectified Flow and Ginzburg-Landau Guidance
Mar 6, 2026Dhruman Gupta, Yashas Shende, Aritra Das +2Physics-Informed ML Inverse Problems
Ashoka University · Department of Computer Science Ashoka University · Department of Physics Ashoka University
Enhancing Physics-Informed Neural Networks with Domain-aware Fourier Features: Towards Improved Performance and Interpretable Results
Mar 3, 2026Alberto Miño Calero, Luis Salamanca, Konstantinos E. TatsisFourier Feature Embeddings Neural Network Interpretability
Department of Engineering Cybernetics NTNU, Trondheim, Norway · Swiss Data Science Center (SDSC) ETH Zürich, Zürich, Switzerland
Pseudo-differential-enhanced physics-informed neural networks
Feb 16, 2026Andrew GracykPDE Solving Spectral Bias
Department of Mathematics, Purdue University, West Lafayette, IN 47907, United States
Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
Feb 10, 2026Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin +2Physics-Informed Generative Modeling PDE 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
Leveraging Differentiable PDE Solvers for Semi-Neural Spatial Reconstruction From Sparse Measurements
Jan 28, 2026Ofek Aloni, Barak FishbainDifferentiable Physics Physics-Informed ML
Department of Mathematics, Technion, Israel · Department of Civil and Environmental Engineering, Technion, Israel
Adaptively trained Physics-informed Radial Basis Function Neural Networks for Solving Multi-asset Option Pricing Problems
Jan 19, 2026Yan Ma, Yumeng Ren, Elisabeth LarssonQuantitative Finance PDE Solving
aCollege of Mathematics and Statistics, Northwest Normal University, Lanzhou, China · bDepartment of Mathematics, City University of Hong Kong, Hong Kong SAR, China · cDepartment of Information Technology, Uppsala University, Sweden
Disentangling Aleatoric and Epistemic Uncertainty in Physics-Informed Neural Networks. Application to Insulation Material Degradation Prognostics
Jan 7, 2026Ibai Ramirez, Jokin Alcibar, Joel Pino +2Bayesian Neural Networks Uncertainty Quantification
Department of Electronics and Computer Science Mondragon University Arrasate-Mondragon, 20500, Spain · CARTIF Technology Centre Valladolid, 47151, Spain · Department of Physical Chemistry University of the Basque Country (EHU)[cs.LG] Basque Centre for Applied Mathematics (BCAM) Leioa, 20018, Spain +1
Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
Jan 1, 2026Abhisek Ganguly, Santosh Ansumali, Sauro SucciDynamical Systems Neural PDE Solvers
Engineering Mechanics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore, 560064, Karnataka, India · Italian Institute of Technology, Viale Regina Elena 291, 00161 Rome, Italy · University of Roma Tre, Italy +2
Evolutionary Physics-Informed Temporal Fusion for Lane-Change Intention Prediction
Dec 30, 2025Jiazhao Shi, Qiyang Xie, Ziyu Wang +7Autonomous Driving Physics-Informed ML
Tandon School of Engineering New York University New York, USA · Khoury College of Computer Science Northeastern University Boston, USA · School of Business Wake Forest University Winston Salem, USA +6
KIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging
Dec 8, 2025Md Raqibul Islam, Adrita Khan, Mir Sazzat Hossain +6Graph Representation Learning Jet Tagging
Center for Computational & Data Sciences, Independent University, Bangladesh, Dhaka-1229, Bangladesh · Department of Theoretical Physics, University of Dhaka, Dhaka-1000, Bangladesh · Department of Physical Sciences, Independent University, Bangladesh, Dhaka-1229, Bangladesh
Neural network-driven domain decomposition for efficient solutions to the Helmholtz equation
Nov 19, 2025Victorita Dolean, Daria Hrebenshchykova, Stéphane Lanteri +1Neural PDE Solvers PDE Solving
Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands · Atlantis project-team, Inria Research Center at Université Côte d’Azur, France · MACARON project-team, Université de Strasbourg, CNRS, Inria, IRMA, France
Hard-constraint physics-residual networks for hydrogen crossover prediction and high-pressure extrapolation in PEM water electrolysis
Nov 8, 2025Yong-Woon Kim, Jihyeok Lee, Chulung Kang +1Neural Surrogate Modeling Physics-Informed ML
Department of Computer Engineering, Jeju National University, Jeju, 63243, South Korea · Faculty of Applied Energy System, Jeju National University, Jeju, 63243, South Korea · Department of Mechanical System Engineering, Jeju National University, Jeju, 63243, South Korea +1
Physics-Informed Neural Networks for Speech Production
Nov 1, 2025Kazuya Yokota, Ryosuke Harakawa, Masaaki Baba +1Speech Processing Physics-Informed ML
Department of Mechanical Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, Japan · Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology
LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries
Oct 29, 2025René P. Klausen, Ivan Timofeev, Jonas Naujoks +4Neural PDE Solvers PDE Solving
Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany · Department of Electrical Engineering and Computer Science, Technische Universität Berlin, Germany · BIFOLD Berlin Institute for the Foundations of Learning and Data, Berlin, Germany +1
Inverse Problem for Partial Differential Equations with Jump Discontinuities in Coefficients by Two-stage Physics-Informed Deep Learning and Statistical Mixture Models
Oct 16, 2025Zhikun Zhang, Guanyu Pan, Xiangjun Wang +2Bayesian Mixture Models Physics-Informed ML
School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710072, China · School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China · SandGold AI Research, Guangzhou 510642, China +3
Neural non-canonical Hamiltonian dynamics for long-time simulations
Oct 2, 2025Clémentine Courtès, Emmanuel Franck, Michael Kraus +2Dynamical Systems Nonlinear System Identification
IRMA, MACARON · Institut de Recherche Mathématique Avancée, UMR 7501 Université de Strasbourg et CNRS, 7 rue René Descartes 672000 Strasbourg, France. · INRIA Nancy-Grand Est, MACARON Project, Strasbourg, France. +5
MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control
Sep 28, 2025Manan Tayal, Aditya Singh, Shishir Kolathaya +1Multi-Agent Control Constrained Optimization
Center for Cyber-Physical Systems, Indian Institute of Science, Bangalore, India · Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, USA · Department of Aeronautics and Astronautics, Stanford University, USA
Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Sep 26, 2025Changhun Kim, Timon Conrad, Redwanul Karim +6Efficient Neural Network Inference Graph Attention Networks
Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany · Institute of Electrical Energy Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
A Flow-rate-conserving CNN-based Domain Decomposition Method for Blood Flow Simulations
Sep 19, 2025Simon Klaes, Axel Klawonn, Natalie Kubicki +4PDE Solving PDE Surrogate Modeling
Department of Mathematics and Computer Science, University of Cologne, Germany · Center for Data and Simulation Science, University of Cologne, Germany · Institute for Advanced Simulation (IAS-2), Forschungszentrum Jülich, Germany +1

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Pure and physics-guided deep learning approaches for spatio-temporal groundwater level prediction

Matteo Salis, Gabriele Sartor, Rosa Meo, Stefano Ferraris+1

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

Physics-Informed ML Attention Mechanisms Spatiotemporal Forecasting

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Groundwater represents a key element of the water cycle, yet it exhibits complex and context-dependent relationships that make its modeling challenging. Theory-based models have been the cornerstone of scientific understanding. However, their computational cost, simplifying assumptions, and calibration requirements limit their use. In recent years, data-driven models have emerged as powerful alternatives. In particular, deep learning has proven to be a promising approach for its design flexibility and ability to learn complex relationships directly from the data without requiring extensive domain information. We proposed an attention-based pure deep learning model, named STAINet, to predict weekly groundwater levels in Piedmont (Italy), leveraging both irregular groundwater time series and weather image sequences. To enhance the model's trustworthiness and generalization ability, we merged the theory and data-driven approaches by considering physics-guided strategies to inject the groundwater flow equation into the model. Firstly, we restructured the tail of the architecture to predict the three terms of the governing equation, named the autoregressive, diffusion, and residual components - we thus obtained the PSTAINet-IB. Then, we further injected physics priors by adding loss terms related to the estimated equation components, obtaining the PSTAINet-ILB model. Lastly, we developed the PSTAINet-ILRB by imposing a loss term specific to the residual component, which forces the groundwater recharge to occur within the groundwater body recharge zone, which is identified by domain experts. The models were evaluated both by feeding true lagged values as input and by iterating their own predictions (rollouts) over the whole test set. The PSTAINet-ILB model performed the best, achieving remarkable test performance, and generating equation components in line with domain experts' expectations.

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Latest papers 642