Physics-Informed ML

ML: Machine Learning

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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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G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
Apr 16, 2026Jack T. Beerman, Tyler J. Abele, Mehdi Taghizadeh +6Neural PDE Solvers PDE Surrogate Modeling
School of Data Science, University of Virginia, Charlottesville, VA 22903, United States · Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22903, United States · Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA 22903, United States +1
Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
Apr 16, 2026Tomasz Służalec, Marcin Łoś, Askold Vilkha +1PDE Solving Physics-Informed ML
AGH University of Krakow, Poland
SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
Apr 16, 2026Murat Furkan Mansur, Tufan KumbasarNeural Surrogate Modeling Parameter Estimation
AI and Intelligent Systems Laboratory, Istanbul Technical University, 34469, Istanbul, Türkiye
Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks
Apr 16, 2026Kang An, Chenhao Si, Shiqian Ma +1Neural Network Optimization Second-Order Optimization
School of Data Science The Chinese University of Hong Kong, Shenzhen Shenzhen, China
Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
Apr 16, 2026Zheng LiuNeural Surrogate Modeling PDE Surrogate Modeling
Department of Industrial and Manufacturing Systems Engineering University of Michigan-Dearborn Dearborn, USA
Material-Agnostic Zero-Shot Thermal Inference for Metal Additive Manufacturing via a Parametric PINN Framework
Apr 16, 2026Hyeonsu Lee, Jihoon JeongNeural Surrogate Modeling Zero-Shot Learning
Wm Michael Barnes ’64 Department of Industrial and Systems Engineering Texas A&M University College Station, TX 77843, USA
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
Stochastic Dimension Implicit Functional Projections for Global Integral Conservation in High-Dimensional PINNs
Mar 31, 2026Zhangyong Liang, Huanhuan GaoNeural 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
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.
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

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G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes

Jack T. Beerman, Tyler J. Abele, Mehdi Taghizadeh, Andrew Davis+5

School of Data Science, University of Virginia, Charlottesville, VA 22903, United States · Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22903, United States · Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA 22903, United States+1

Neural PDE Solvers PDE Surrogate Modeling Physics-Informed ML Physics-Informed GNNs Spatiotemporal GNNs

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Physics-aware recurrent convolutional networks (PARC) have demonstrated strong performance in predicting nonlinear spatiotemporal dynamics by embedding differential operators directly into the computational graph of a neural network. However, pixel-based convolutions are restricted to static, uniform Cartesian grids, making them ill-suited to following evolving localized structures in an efficient manner. Graph neural networks (GNNs) naturally handle irregular spatial discretizations, but existing graph-based physics-aware deep learning (PADL) methods have difficulty handling extreme nonlinear regimes. To address these limitations, we propose Graph PARC (G-PARC), which uses moving least squares (MLS) kernels to approximate spatial derivatives on unstructured graphs, and embeds the derivatives of governing partial differential equations into the network's computational graph. G-PARC achieves better accuracy with 2-3x fewer parameters than MeshGraphNet, MeshGraphKAN, and GraphSAGE, replacing the traditional encoder-processor-decoder framework with analytically computed differential operators. We demonstrate that G-PARC (1) generalizes across nonuniform spatial and temporal discretizations; (2) handles moving meshes required for structural deformation; and (3) outperforms existing graph-based PADL methods on nonlinear benchmarks including fluvial hydrology, planar shock waves, and elastoplastic dynamics. By embedding explicit physical operators within the flexibility of GNNs, G-PARC enables accurate modeling of extreme nonlinear phenomena on complex computational domains, moving PADLbeyond idealized Cartesian grids.

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Latest papers 561