Learning in infinite dimension with neural operators.

A library for Koopman Neural Operator with Pytorch.

This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs

Automatic Functional Differentiation in JAX

Codomain attention neural operator for single to multi-physics PDE adaptation.

ICML2024: Equivariant Graph Neural Operator for Modeling 3D Dynamics

Official implementation of Scalable Transformer for PDE surrogate modelling

[ICLR 2025] Neural Operator-Assisted Computational Fluid Dynamics in PyTorch

A multiphase multiphysics dataset and benchmarks for scientific machine learning

This repository contains the code for the paper: Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation (IEEE TPAMI 2025)

An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.

Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning

Official implementation of the paper "Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?"

The first global synthetic dataset for physics-ML seismic wavefield modeling and full-waveform inversion

This repository contains the code for the paper: Deciphering and integrating invariants for neural operator learning with various physical mechanisms, National Science Review, 2024

Code for the paper "The Random Feature Model for Input-Output Maps between Banach Spaces" (SIREV SIGEST 2024, SISC 2021)

Neural Operators with Applications to the Helmholtz Equation

Code for ICML 24 paper "Implicit Representations via Operator Learning"

Code for the paper ``Error Bounds for Learning with Vector-Valued Random Features'' (NeurIPS 2023, Spotlight)

Implementation of neural operator papers in PyTorch for easier usage. Achieve SOTA in PDE prediction.