Physics-Informed Neural Operators

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Overview

Physics-informed neural operators learn maps from inputs such as initial conditions, parameters, or geometries to solutions of partial differential equations, so one trained model can handle many related problems. Common approaches include Fourier Neural Operators and DeepONets trained with equation-residual or weak-form losses, alongside architectures that build in properties such as conservation, incompressibility, or energy dissipation. These surrogates aim to reduce the cost of repeated simulations and the need for labeled solutions in settings such as fluid flow, phase-field evolution, Poisson problems, and elasticity. A central challenge is balancing physical fidelity and generalization against training stability and discretization mismatch: constraints can help, but may introduce bias when they do not hold for the data or resolution being modeled.

Latest papers

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72 papers12 papers in the last four weeks, up 20% on the four weeks before. 0.1% of all new papers.

  1. Physics-informed neural operators for the in situ characterization of locally reacting sound absorbers

    Apr 8, 2026Jonas M. Schmid, Johannes D. Schmid, Martin Eser +1Inverse ProblemsPhysics-Informed Neural Operators

    Chair of Vibroacoustics of Vehicles and Machines, Technical University of Munich, Garching, 85748, Germany · Independent Researcher, Munich, 81377, Germany

  2. Project and Generate: Divergence-Free Neural Operators for Incompressible Flows

    Mar 25, 2026Xigui Li, Hongwei Zhang, Ruoxi Jiang +6Neural OperatorsComputational Fluid Dynamics

    Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China · Shanghai Academy of Artificial Intelligence for Science, Shanghai, China

  3. SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications

    Mar 23, 2026Shailesh Garg, Luis Mandl, Somdatta Goswami +1Physics-Informed Neural OperatorsNeural Operators

    Department of Applied Mechanics Indian Institute of Technology Delhi Hauz Khas, New Delhi 110016, India. · Institute of Structural Mechanics and Dynamics in Aerospace Engineering Faculty of Aerospace Engineering and Geodesy University of Stuttgart, Stuttgart, Germany. · Experimental Hepatobiliary Surgery Group Department of Hepatobiliary Surgery and Visceral Transplantation University of Leipzig Medical Center, Leipzig, Germany. +3

  4. Physics-informed neural operator for parametric phase-field modelling of interfacial degradation and microstructural evolution

    Mar 10, 2026Nanxi Chen, Airong Chen, Rujin MaPhysics-Informed Neural OperatorsPDE Surrogate Modeling

    College of Civil Engineering, Tongji University, Shanghai 200092, China

  5. Physics-informed AI Accelerated Retention Analysis of Ferroelectric Vertical NAND: From Day-Scale TCAD to Second-Scale Surrogate Model

    Mar 6, 2026Gyujun Jeong, Sungwon Cho, Minji Shon +13Neural Surrogate ModelingPhysics-Informed Neural Operators

    School of Electrical and Computer Engineering, Georgia Institute of Technology, GA, USA · Semiconductor Research and Development, Samsung Electronics Co., Ltd, South Korea · NVIDIA, Santa Clara, CA, USA

  6. From Complex Dynamics to DynFormer: Rethinking Transformers for PDEs

    Mar 3, 2026Pengyu Lai, Yixiao Chen, Dewu Yang +3PDE Surrogate ModelingNeural Operators

    School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, China

  7. Tackling multiphysics problems via finite element-guided physics-informed operator learning

    Mar 2, 2026Yusuke Yamazaki, Reza Najian Asl, Markus Apel +2Multiphysics SimulationPhysics-Informed Neural Operators

    Graduate School of Science and Technology, Keio University, Hiyoshi3-14-1,Kohoku-ku, Yokohama 223-8522, Japan · Chair of Structural Analysis, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany · ACCESS e.V., Intzestr. 5, 52072 Aachen, Germany +1

  8. Learning Physical Operators using Neural Operators

    Feb 26, 2026Vignesh Gopakumar, Ander Gray, Dan Giles +5PDE Surrogate ModelingPhysics-Informed Neural Operators

    UCL Centre for Artificial Intelligence · UK Atomic Energy Authority · LIX, CNRS, ´Ecole Polytechnique +2

  9. Muon with Spectral Guidance: Efficient Optimization for Scientific Machine Learning

    Feb 18, 2026Binghang Lu, Jiahao Zhang, Guang LinMuon OptimizerPhysics-Informed ML

    Department of Mathematics, Purdue University, West Lafayette, IN 47907, USA · Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA

  10. Imaging-Derived Coronary Fractional Flow Reserve: Advances in Physics-Based, Machine Learning, and Physics-Informed Methods

    Feb 17, 2026Tanxin Zhu, Emran Hossen, Chen Zhao +4Medical Image AnalysisPhysics-Informed ML

    Department of Applied Computing, Michigan Technological University, Houghton, MI, USA · Department of Computer Science, Kennesaw State University, Marietta GA, USA · Department of Biomedical Engineering, Michigan Technological University, Houghton, MI, USA +3

  11. Learning Data-Efficient and Generalizable Neural Operators via Fundamental Physics Knowledge

    Feb 16, 2026Siying Ma, Mehrdad M. Zadeh, Mauricio Soroco +3PDE Surrogate ModelingNeural Operators

    Simon Fraser University · Georgia Institute of Technology

  12. FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation

    Nov 29, 2022Adarsh Ganeshram, Valentin Duruisseaux, Haydn Maust +6Physics-Informed Neural OperatorsPDE Solving

    Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA