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

ICML2024: Equivariant Graph Neural Operator for Modeling 3D Dynamics

Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks

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

No need to train, he's a smooth operator

Datasets and code for results presented in the BOON paper

Official implementation of Scalable Transformer for PDE surrogate modelling

Neural Operator-Assisted Computational Fluid Dynamics in PyTorch

A multiphase multiphysics dataset and benchmarks for scientific machine learning

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

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

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

Official implementation of the NeurIPS 23 spotlight paper of ♾️InfGCN♾️.

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

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

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