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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Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing
May 14, 2026Ellwil Sharma, Arastu SharmaSparse Mixture-of-Experts PDE Operator Learning
Shodh AI
Compositional Neural Operators for Multi-Dimensional Fluid Dynamics
May 12, 2026Hamda Hmida, Hsiu-Wen Chang, Youssef MesriPDE Operator Learning Physics-Informed Neural Operators
Mines Paris - PSL University, Centre for Material Forming (CEMEF) · Mines Paris - PSL University, Centre for Robotics (CAOR)
jNO: A JAX Library for Neural Operator and Foundation Model Training
May 11, 2026Leon Armbruster, Rathan Ramesh, Georg Kruse +1PDE Operator Learning Physics-Informed Neural Operators
Fraunhofer Institute for Integrated Systems and Device Technology IISB, Germany
CATO: Charted Attention for Neural PDE Operators
May 9, 2026Chun-Wun Cheng, Sifan Wang, Carola-Bibiane Schönlieb +1PDE Operator Learning Attention Mechanisms
DAMTP University of Cambridge · Institute for Foundations of Data Science Yale University · Yau Mathematical Sciences Center Tsinghua University
Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
May 8, 2026Hamidreza Eivazi, Henning WesselsStructural Mechanics Hyperelasticity
Division Data-Driven Modeling of Mechanical Systems, Institute of Applied Mechanics, Technische Universität Braunschweig, Pockelsstr. 3, 38106 Braunschweig, Germany
Replay-Based Continual Learning for Physics-Informed Neural Operators
May 6, 2026Yizheng Wang, Mohammad Sadegh Eshaghi, Xiaoying Zhuang +2OOD Generalization Replay-Based Continual Learning
Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China · Institute of Photonics, Department of Mathematics and Physics, Leibniz University Hannover, Germany · Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstr. 15, D-99423 Weimar, Germany
Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries
May 1, 2026Rodolphe Barlogis, Ferhat Tamssaouet, Quentin Falcoz +1PDE Solving PDE Operator Learning
PROMES-CNRS, Université Perpignan Via Domitia (UPVD), Perpignan, France · LAAS-CNRS, Université de Toulouse (UT), Toulouse, France
PILIR: Physics-Informed Local Implicit Representation
May 1, 2026Jianfeng Li, Feng Wang, Ke TangNeural PDE Solvers Implicit Neural Representations
School of Computer Science, Wuhan University · School of Computer Science and Technology, Southern University of Science and Technology
Learning Neural Operator Surrogates for the Black Hole Accretion Code
Apr 28, 2026Matthias Nägele, Cedric Bös, Chester Tan +3Fourier Neural Operator PDE Surrogate Modeling
Institute for Theoretical Physics and Astrophysics, Julius-Maximilians-Universität Würzburg, Würzburg, Germany · Chair for Machine Learning for Complex Networks, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
Droplet-LNO: Physics-Informed Laplace Neural Operators for Accurate Prediction of Droplet Spreading Dynamics on Complex Surfaces
Apr 22, 2026Ganesh Sahadeo Meshram, Partha Pratim Chakrabarti, Suman ChakrabortyPDE Operator Learning Physics-Informed Neural Operators
Department of Mechanical Engineering, IIT Kharagpur, Kharagpur, India · Department of Computer Science & Engineering, IIT Kharagpur, Kharagpur, India
DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting
Apr 20, 2026Chih-Kang Huang, Ludovick Gagnon, Miha Založnik +1Physics-Informed Neural Operators Neural Operators
1Universit´e de Lorraine, CNRS, IJL, F-54000 Nancy, France · 2Universit´e de Lorraine, CNRS, Inria, IECL, F-54000 Nancy, France

May 14, 2026, cs.LGJ/K move · Enter open · S save

Eradicating Negative Transfer in Multi-Physics Foundation Models via Sparse Mixture-of-Experts Routing

Ellwil Sharma, Arastu Sharma

Shodh AI

Sparse Mixture-of-Experts PDE Operator Learning Operator Learning Physics-Informed Neural Operators Negative Transfer

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Scaling Scientific Machine Learning (SciML) toward universal foundation models is bottlenecked by negative transfer: the simultaneous co-training of disparate partial differential equation (PDE) regimes can induce gradient conflict, unstable optimization, and plasticity loss in dense neural operators. In particular, broadband open-channel fluid dynamics and boundary-dominated porous media flows impose incompatible spectral and geometric demands on a single dense parameter path. We introduce Shodh-MoE, a sparse-activated latent transformer architecture for multi-physics transport. Shodh-MoE operates on compressed 16^3 physical latents produced by a physics-informed autoencoder with an intra-tokenizer Helmholtz-style velocity parameterization, restricting decoded states to divergence-free velocity manifolds. The model guarantees exact mass conservation, achieving a physically verifiable velocity divergence of ~2.8 x 10^-10 (evaluated post-hoc in FP64) on 128^3 grids. A Top-1 soft-semantic router dynamically assigns localized latent patches to expert subnetworks, enabling specialized parameter paths for distinct physical mechanisms while preserving shared experts for universal symmetries. In a 20,000-step distributed pretraining run over mixed three-dimensional physical tensors, routing telemetry shows autonomous domain bifurcation: held-out validation tokens from the open-channel domain route exclusively to Expert 0, while porous-media tokens route exclusively to Expert 1. The model converges simultaneously across both regimes, achieving latent validation MSEs of 2.46 x 10^-5 and 9.76 x 10^-6, and decoded physical MSEs of 2.48 x 10^-6 and 1.76 x 10^-6. These results support sparse expert routing as a practical architectural mechanism for mitigating multi-physics interference in universal neural operators.

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