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

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

No need to train, he's a smooth operator

Datasets and code for results presented in the BOON paper

ICML2024: Equivariant Graph Neural Operator for Modeling 3D Dynamics

Official implementation of Scalable Transformer for PDE surrogate modelling

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

Neural Operator-Assisted Computational Fluid Dynamics in PyTorch

A multiphase multiphysics dataset and benchmarks for scientific machine learning

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

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 NeurIPS 23 spotlight paper of ♾️InfGCN♾️.

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

Neural Operators with Applications to the Helmholtz Equation