A Library for Learning Neural Operators
Machine Learning
2026-02-02 v5 Artificial Intelligence
Abstract
We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces. They can be trained and inferenced on input and output functions given at various discretizations, satisfying a discretization convergence properties. Part of the official PyTorch Ecosystem, NeuralOperator provides all the tools for training and deploying neural operator models, as well as developing new ones, in a high-quality, tested, open-source package. It combines cutting-edge models and customizability with a gentle learning curve and simple user interface for newcomers.
Keywords
Cite
@article{arxiv.2412.10354,
title = {A Library for Learning Neural Operators},
author = {Jean Kossaifi and Nikola Kovachki and Zongyi Li and David Pitt and Miguel Liu-Schiaffini and Robert Joseph George and Boris Bonev and Kamyar Azizzadenesheli and Julius Berner and Valentin Duruisseaux and Anima Anandkumar},
journal= {arXiv preprint arXiv:2412.10354},
year = {2026}
}