Physics-Informed GNNs

GNN: Graph Neural Network

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Overview

Physics-informed graph neural networks use graph structure to represent meshes, networks, or interacting entities while incorporating known physical laws into learning. Common approaches include adding equation-residual or conservation losses, building physical operators directly into message passing, and combining learned components with numerical solvers or variational energy methods. These models are used as fast surrogates for tasks such as fluid and solid mechanics, power flow, and transport on irregular or changing topologies. A central challenge is ensuring that low prediction error also means physical consistency, while maintaining reliable transfer across new geometries, operating conditions, and system sizes.

Latest papers

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40 papers8 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

  1. Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network

    Oct 24, 2026Chenghao Huang, Yue Yang, Hao WangPower SystemsSafe RL

    Department of Data Science and AI, Faculty of IT and Monash Energy Institute, Monash University, Melbourne, VIC 3800, Australia

  2. Graph Neural Networks for Astrophysics and Cosmology

    Oct 19, 2026Christian Kragh JespersenGraph Neural NetworksPhysics-Informed GNNs

    Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA

  3. PhyPro-FM: A Physics and Prompt-Guided Graph Foundation Model for Contingency Analysis

    Oct 19, 2026Xu Yang, Zhanyuan Sheng, Chenhui Lin +4Power SystemsGraph Foundation Models

    State Key Laboratory of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing 100084, China. · Hong Kong Polytechnic University, Hong Kong 999077, China. · State Key Laboratory of High-Efficiency and High-Quality Conversion for Electric Power, Hefei University of Technology, Hefei 230009, China.

  4. PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting

    Oct 13, 2026Jelena Simeunović, Baptiste Schubnel, Pierre-Jean Alet +2Physics-Informed GNNsSpatiotemporal Forecasting

    CSEM, Rue de Jaquet-Droz 1, 2002 Neuchâtel, Switzerland · Department of Electrical Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Rte Cantonale, 1015 Lausanne, Switzerland.

  5. A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration

    Oct 12, 2026Wenpu Du, Peng Zhou, Yunlong Xia +5Graph Neural OperatorsNeural Operators

    School of Environment and Safety Engineering, North University of China, Taiyuan, 030051, China

  6. Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flow

    Oct 5, 2026Changhun Kim, Timon Conrad, Redwanul Karim +7Physics-Informed GNNsPower Systems

    Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany · Institute of Electrical Energy Systems, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

  7. Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design

    Oct 4, 2026Avi Epstein, Snir Nehemia, Haim Suchowski +1Physics-Informed GNNsGraph Transformer

    Tel Aviv University

  8. Physics-Refined Spatiotemporal Forecasting on Open-Boundary Hydrologic Graphs

    Oct 1, 2026Haoyang Jiang, Zhengui Wang, Shenghan Gao +3Spatiotemporal ForecastingPhysics-Informed GNNs

    William & Mary, Williamsburg, VA, USA · Virginia Institute of Marine Science, William & Mary, Gloucester Point, VA, USA · Florida Atlantic University, Boca Raton, FL, USA

  9. GridSFM: A Foundation Model for Solving AC Optimal Power Flow

    Sep 24, 2026Luke Bhan, Weiwei Yang, Margaret Capetz +1Neural Surrogate ModelingOptimal Power Flow

    University of California, San Diego · Microsoft Research · University of Washington

  10. Learning coarse-step dynamics and internal mechanical response with graph networks

    Sep 24, 2026Vinay Sharma, Olga FinkPhysics-Informed GNNsSystem Identification

    Intelligent Maintenance and Operations Systems, EPFL, Lausanne, Switzerland

  11. A variational physics-informed graph neural network for heterogeneous solid mechanics

    Sep 10, 2026Aashay Rajan Yadav, Amiya Prakash Das, Ratna Kumar AnnabattulaNeural PDE SolversStructural Mechanics

    Mechanics of Materials Lab, Department of Mechanical Engineering, Indian Institute of Technology Madras, Chennai, 600036, Tamil Nadu, India

  12. Mesh-Native Physics-Informed Graph Surrogates for TCAD-in-the-Loop Design Space Exploration

    Sep 2, 2026Leonid Popryho, Ayoub Sadeghi, Inna Partin-VaisbandPDE Surrogate ModelingPhysics-Informed ML

    University of Illinois Chicago · Chicago, IL, USA

  13. Conservative Hybrid Graph Networks for Process Systems with Learned Routing

    Aug 28, 2026Paolo GuidaPhysics-Informed GNNsSystem Identification

    Physical Science and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal, Saudi Arabia

  14. Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields

    Jul 22, 2026Tianyu Li, Zhiwei Cao, Qingang Zhang +3Neural Surrogate ModelingGraph Neural Networks

    Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore, Singapore. · School of Future Technology, Fuzhou University, Fuzhou, China. · College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore. +2

  15. MeGA-MP: Metric Graph Advection Message Passing -- A Physics-Informed Message Passing Operator for Advection-Dominated Metric Graphs

    Jul 6, 2026Janine Strotherm, Luca Hermes, André Artelt +1Graph Representation LearningPDE Solving

    Bielefeld University

  16. Physics-Guided Dual-Stream Heterogeneous Graph Neural Network for Predicting Full-Field Structural Response of Stiffened Panels

    Jun 18, 2026Yuecheng Cai, Jasmin JelovicaNeural Surrogate ModelingGraph Neural Networks

    Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada · Department of Civil Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada

  17. Physics-Aware Auxiliary Losses Improve Out-of-Distribution Generalization of a GNN Synthesizability Filter

    Jun 10, 2026Riya Bisht, Dhruv AgarwalGraph Neural NetworksOOD Generalization

  18. Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks

    Jun 9, 2026Manuel Ricardo Guevara Garban, Yves Chemisky, Étienne Prulière +3Neural Surrogate ModelingStructural Mechanics

    Univ. Bordeaux, CNRS, Bordeaux INP, I2M, UMR 5295, Talence, France · Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, Talence, France · Institute of Mechanics and Fluid Dynamics, TU Bergakademie Freiberg, Freiberg, 09599, Germany +2

  19. Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments

    May 19, 2026Takshak Shende, Viktor PopovPDE Surrogate ModelingPhysics-Informed ML

    Department of Mechanical Engineering, University College London (UCL), London, United Kingdom · Ascend Technologies Ltd, Southampton, United Kingdom

  20. Learning Discrete Riemannian Metrics for Physical Fields with Cochain-Frame Equivariance

    May 14, 2026Dongzhe Zheng, Christine Allen-BlanchetteRiemannian Metric LearningPhysics-Informed GNNs

    1Princeton University

  21. Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions

    May 4, 2026Sergio Orozco, Tushar Kusnur, Brandon May +2Deformable Object ManipulationRobotic Manipulation

    Brown University, Providence, RI · Robotics and AI (RAI) Institute, Cambridge, MA · General Motors

  22. Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data

    Apr 26, 2026Ru Geng, Panayotis Kevrekidis, Yixian Gao +2Dynamical SystemsGraph Structure Learning

    School of Mathematics and Statistics, Changchun University, Changchun, 130022, China · Department of Mathematics and Statistics, University of Massachusetts, Amherst, 01003, MA, USA · Department of Physics, University of Massachusetts, Amherst, 01003, MA, USA +2

  23. Zero-Shot Scalable Resilience in UAV Swarms: A Decentralized Imitation Learning Framework with Physics-Informed Graph Interactions

    Apr 17, 2026Huan Lin, Lianghui DingMulti-Robot SystemsSwarm Robotics

    Institute of Image Communication and Network Engineering, School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai 200240, China

  24. 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 SolversPDE 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

  25. Hypergraph Neural Diffusion: A PDE-Inspired Framework for Hypergraph Message Passing

    Apr 13, 2026Zhiheng Zhou, Mengyao Zhou, Xixun Lin +2Hypergraph Neural NetworksHypergraph Learning

    School of Mathematics and Statistics, Shandong University · Academy of Mathematics and Systems Science, Chinese Academy of Sciences · School of Mathematical Sciences, University of Chinese Academy of Sciences +2

  26. K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data

    Apr 10, 2026Zesheng Liu, Maryam RahnemoonfarSpatiotemporal GNNsPhysics-Informed GNNs

    Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, 18015, Pennsylvania, USA · Department of Civil and Environmental Engineering, Lehigh University, 19 Memorial Drive W., Bethlehem, 18015, Pennsylvania, USA

  27. Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction

    Apr 9, 2026Tong Duy Son, Kohta Sugiura, Marc Brughmans +7Graph Representation LearningPhysics-Informed GNNs

    The authors are with Siemens Digital Industries Software, Leuven, Belgium.

  28. Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks

    Apr 9, 2026Eunjeong Park, Amrita BasakComputational Materials SciencePhysics-Informed GNNs

    Department of Mechanical Engineering, Pennsylvania State University, University Park, PA, 16802, USA

  29. Graph Neural ODE Digital Twins for Control-Oriented Reactor Thermal-Hydraulic Forecasting Under Partial Observability

    Apr 8, 2026Akzhol Almukhametov, Doyeong Lim, Rui Hu +1Surrogate ModelingDigital Twins

    Department of Nuclear Engineering, Texas A&M University, College Station, TX 77843, USA · Argonne National Laboratory, Nuclear Science and Engineering Division, USA

  30. From Physics to Surrogate Intelligence: A Unified Electro-Thermo-Optimization Framework for TSV Networks

    Mar 31, 2026Mohamed Gharib, Leonid Popryho, Inna Partin-VaisbandElectronic Design AutomationSurrogate Modeling

    Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL 60607, USA