Physics-Informed Neural Operators

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7 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Inferring physical fields in coupled systems with unknown parameters from incomplete observations using physics-constrained attentive neural operators
Oct 5, 2026Shilun Wei, Xiaoqiang Sun, Wei Li +1Parameter Identifiability Physics-Informed Neural Operators
School of Mathematics, Sun Yat-sen University, Guangzhou, China · School of Sciences, Great Bay University, Dongguan, China · John Hopcroft Center for Computer Science, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China
Poisson-GENERIC Neural Operators: Exact Metriplectic Structure in Function Space via Casimir Entropies
Oct 4, 2026Jason Sulskis, Sathya RaviDynamical Systems PDE Operator Learning
Georgia Tech Research Institute, Atlanta, GA, USA
Green-Routed Neural Operators:\Physics Determines Where the Network Reads
Oct 4, 2026Chenhao Si, Ming YanPDE Surrogate Modeling PDE Operator Learning
School of Data Science The Chinese University of Hong Kong, Shenzhen Shenzhen, China
When Known Physics Helps Neural PDE Models: Residual Constraints Out-Regularize Generic Priors for Nonlinear Dynamics
Sep 27, 2026Zahra Farazpay, Aniruddha BoraPDE Surrogate Modeling Physics-Informed ML
Department of Physics Louisiana Tech University Ruston, LA, USA · Department of Computer Science Texas State University San Marcos, TX, USA
Physics and Data Driven Transformer-Mamba Framework for Flow Field
Sep 24, 2026Zhuo Zhang, Shun Zou, Canqun Yang +1PDE Surrogate Modeling PDE Operator Learning
College of Computer Science and Technology, National University of Defense Technology, Changsha, China · College of Artificial Intelligence, Nanjing Agricultural University, NanJing, China · National SuperComputer Center in Tianjin, Binhai New Area, Tianjin, China +1
Learning Physics from an Imperfect Ancestor
Sep 21, 2026S. Mohammad Mousavi, Teeratorn Kadeethum, Nikolaos Bouklas +1PDE Surrogate Modeling OOD Generalization
Sibley School of Mechanical and Aerospace Engineering, Cornell University · AI Lab, Siemens Energy · Department of Civil and Systems Engineering, Johns Hopkins University
Real-time Generalizable Heart Valve Mechanics for Clinical Disease Assessment via a Physics-Conditioned Neural Operator
Sep 20, 2026Shawn Koohy, Wensi Wu, Matthew A Jolley +1Surrogate Modeling Physics-Informed Neural Operators
Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA. · Cardiovascular Institute, Children’s Hospital of Philadelphia, Philadelphia, PA, USA. · Department of Anesthesiology and Critical Care Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA. +1
Where to Compute and How to Interact: Operator-Readable Adaptation with Gauge-Aware Transport
Sep 14, 2026Zixuan Shen, Quanxu Wan, Bingchuan Wang +2Adaptive Mesh Refinement Physics-Informed Neural Operators
Central South University · Nanjing University
GenONet: A Generative operator Network for High-Resolution Precipitation Nowcasting
Sep 1, 2026Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohammad KhanjaniPrecipitation Forecasting Spatiotemporal Forecasting
University of Illinois Urbana-Champaign · Sharif University of Technology
HIPNO: Symmetry-Aware Physics-Informed Neural Operators for Noninvasive Hemodynamic Inference
Aug 8, 2026Yunbei Pan, Jiahang Sha, Simon A. Lee +3Physics-Informed Neural Operators
Department of Computational Medicine, UCLA · Scalable Analytics Institute (ScAi), Department of Computer Science, UCLA · Department of Anesthesiology & Perioperative Medicine, UCLA +1
A Physics-Informed Hybrid Neural Operator for Transient Magnetization Prediction in Power Magnetics
Aug 3, 2026Yachao Zhu, Qiujie Huang, Sinan Li +3Materials Property Prediction Physics-Informed Neural Operators
School of Electrical and Computer Engineering, the University of Sydney, Darlington, NSW, 2008, Australia · National Railway Research and Design Institute of Signal and Communication, Beijing 100070, China
SpectONet: A Physics-Guided Spectral Deep Operator Network for Euler-Bernoulli Beam Dynamics
Jul 28, 2026Shivani Saini, Ramesh Kumar Vats, Arup Kumar SahooStructural Mechanics PDE Operator Learning
Department of Mathematics and Scientific Computing, National Institute of Technology Hamirpur, 177005, Himachal Pradesh, India · The Hatter Department of Marine Technologies, Leon H. Charney School of Marine Sciences, University of Haifa, 3498838, Haifa, Israel
A Physics-Informed Neural Operator for Thermal Ranking of Low-Cost Wall Materials in Hot-Dry Climates
Jul 28, 2026Muhammad Akbar Khan, Fahim Raees, Ubaida FatimaPDE Surrogate Modeling Physics-Informed Neural Operators
Department of Mathematics, NED University of Engineering and Technology, Karachi 75270, Pakistan
Physics-Informed Neural Operator for Warm-Starting Background-Decomposed and Preconditioned PSFD: Enabling Scalable 3-D EUV Mask Simulation
Jul 28, 2026Doyun Kim, Werner GillijnsFourier Neural Operator Semiconductor Manufacturing
IMEC, Kapeldreef 75, 3001 Leuven, Belgium
Generalized Neural Operator for Parametric and Boundary-Value Problems
Jul 24, 2026Ruoyan Li, Yizhou Sun, Wei WangEfficient Neural Network Inference PDE Solving
University of California, Los Angeles
Learning Physics-Informed Surrogate Model of Linear Elastic Displacement Fields from Geometry
Jul 10, 2026Rodolphe Barlogis, Ferhat Tamssaouet, Quentin Falcoz +1Structural Mechanics PDE Surrogate Modeling
PROMES-CNRS, Universit´e Perpignan Via Domitia (UPVD), Perpignan, France · LAAS-CNRS, Universit´e de Toulouse (UT), Toulouse, France
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 Learning PDE Solving
Bielefeld University
Temperature Field Reconstruction of Tungsten Monoblock Divertor on EAST using Physics-aware Neural Operator Transformer
Jun 30, 2026Zikang Yan, Xiao Wang, Qingquan Yang +6PDE Surrogate Modeling Physics-Informed Neural Operators
School of Computer Science and Technology, Anhui University, Hefei, China · Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, China · Tongling University, Tongling 244000, China +2
Physics-Informed Neural Operator for Speech Production Analysis
Jun 21, 2026Kazuya Yokota, Xinmeng Luan, Debasish Ray Mohapatra +2Speech Processing PDE Operator Learning
Department of Mechanical Engineering, Nagaoka University of Technology, Japan · Schulich School of Music, McGill University, Canada · Department of Electrical and Computer Engineering, University of British Columbia, Canada
MR-GVNO: A Geometry-Aware Variational Physics-Informed Neural Operator for Mindlin-Reissner Plates on Irregular Domains
Jun 15, 2026Siqi Wang, Daobo Sun, Yizheng Wang +4PDE Surrogate Modeling PDE Operator Learning
Institute of Computational Mechanics × AI & College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai 200433, China · School of Aerospace Engineering, Xiamen University, Xiamen 361102, China · Department of Engineering Mechanics, Tsinghua University, Beijing, 100084, China +2
HAMNO: A Hierarchical Adaptive Multi-scale Neural Operator with Physics-Informed Learning for Dynamical Systems
Jun 10, 2026Mostafa Bamdad, Mohammad Sadegh Eshaghi, Timon RabczukNeural PDE Solvers Physics-Informed Neural Operators
Chair of Computational Mechanics, Institute of Structural Mechanics, Bauhaus-Universität Weimar, 99423 Weimar, Germany · Chair of Computational Science and Simulation Technology, Institute of Photonics, Department of Mathematics and Physics, Leibniz University Hannover, 30167 Hannover, Germany
GENERIC-FNO: Embedding Energy Conservation and Entropy Production into Fourier Neural Operators
Jun 6, 2026Jason Sulskis, Sathya RaviFourier Neural Operator Physics-Informed Neural Operators
Department of Computer Science University of Illinois at Chicago · Electronic Systems Laboratory, Applied Embedded Systems Division Georgia Tech Research Institute
On the training of physics-informed neural operators for solving parametric partial differential equations
Jun 4, 2026Nanxi Chen, Chuanjie Cui, Airong Chen +2Neural Network Optimization Gradient Interference
College of Civil Engineering, Tongji University, Shanghai 200092 · Department of Engineering Science, University of Oxford, Oxford OX1 3PJ · Institute for Foundations of Data Science, Yale University, New Haven, CT 06520
FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery
Jun 1, 2026Junhyuk Heo, Junhwan Park, Sancheol Sim +2Edge Inference Remote Sensing
TelePIX, Seoul, Republic of Korea
Sequential Physics-Constrained Neural Operator Forward Modeling for the $\textit{Norne}$ Reservoir System
May 27, 2026Clement Etienam, Juntao Yang, Oleg Ovcharenko +4Fourier Neural Operator PDE Surrogate Modeling
NVIDIA Corporation
NPSolver: Neural Poisson Solver with Iterative Physics Supervision
May 25, 2026Bocheng Zeng, Rui Zhang, Runze Mao +5Neural PDE Solvers PDE Solving
Gaoling School of Artificial Intelligence Renmin University of China Beijing, China · School of Engineering Science University of Chinese Academy of Sciences Beijing, China
WINO: A Weak-Form Physics Informed Neural Operator for Hyperelasticity on Variable Domains
May 23, 2026Bokai Zhu, Qinghui Zhang, Timon RabczukNeural PDE Solvers PDE Solving
School of Science, Harbin Institute of Technology, Shenzhen, P. R. China. · School of Science, Harbin Institute of Technology, Shenzhen, Guangdong. · Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, 99423, Germany.
Discontinuous Galerkin Neural Operator for Pathology Defocus Deblurring
May 22, 2026Shaoqing Duan, Haofei Song, Xintian Mao +2Computational Pathology Image Deblurring
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China.
Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review
May 21, 2026Joseph NyangonPhysics-Informed ML Physics-Informed Neural Operators
Energy Exemplar, Salt Lake City, UT, 84111, USA
fPINN-DeepONet: A Physics-Informed Operator Learning Framework for Multi-term Time-fractional Mixed Diffusion-wave Equations
May 15, 2026Binghang Lu, Zhaopeng Hao, Christian Moya +1PDE Solving Deep Operator Networks
Department of Mathematics, Purdue University West Lafayette, IN 47907, USA · School of Mechanical Engineering, Purdue University West Lafayette, IN 47907, USA

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Inferring physical fields in coupled systems with unknown parameters from incomplete observations using physics-constrained attentive neural operators

Shilun Wei, Xiaoqiang Sun, Wei Li, Kejun Tang

School of Mathematics, Sun Yat-sen University, Guangzhou, China · School of Sciences, Great Bay University, Dongguan, China · John Hopcroft Center for Computer Science, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China

Parameter Identifiability Physics-Informed Neural Operators PDE Inverse Problems Inverse Problems

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Given incomplete measurements of a single physical field in a coupled system with unknown parameters, can we infer its full physical state and identify the underlying parameters? This problem is challenging because multiple coupled fields must be reconstructed simultaneously from limited observations of only one, while the system parameters are unknown. In this work, we propose a machine learning framework for full-field reconstruction and parameter identification of unknown physical systems from sparse observations of a single physical field. Specifically, the cross-attention encoder propagates sparse sensor observations onto a regular grid to construct a sensor-conditioned latent representation, while a Fourier neural operator (FNO) decoder captures global spatial dependencies to reconstruct all coupled physical fields. The network parameters and unknown physical parameters are jointly optimized by minimizing observation losses, governing equation residuals, and boundary/initial condition constraints. The proposed approach is validated on two- and three-dimensional lid-driven cavity flows, a two-dimensional cylinder wake, and a two-dimensional non-ideal magnetohydrodynamics problem, demonstrating the recovery performance of unobserved fields and physical parameters from incomplete observations.

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