JMLR
A Library for Learning Neural Operators
Authors
Kamyar Azizzadenesheli
Jean Kossaifi
Anima Anandkumar
Nikola Kovachki
Zongyi Li
David Pitt
Miguel Liu-Schiaffini
Robert J. George
Boris Bonev
Julius Berner
Valentin Duruisseaux
Paper Information
-
Journal:
Journal of Machine Learning Research -
Added to Tracker:
Sep 09, 2026
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 and researchers.
Author Details
Kamyar Azizzadenesheli
AuthorJean Kossaifi
AuthorAnima Anandkumar
AuthorNikola Kovachki
AuthorZongyi Li
AuthorDavid Pitt
AuthorMiguel Liu-Schiaffini
AuthorRobert J. George
AuthorBoris Bonev
AuthorJulius Berner
AuthorValentin Duruisseaux
AuthorCitation Information
APA Format
Kamyar Azizzadenesheli
,
Jean Kossaifi
,
Anima Anandkumar
,
Nikola Kovachki
,
Zongyi Li
,
David Pitt
,
Miguel Liu-Schiaffini
,
Robert J. George
,
Boris Bonev
,
Julius Berner
&
Valentin Duruisseaux
.
A Library for Learning Neural Operators.
Journal of Machine Learning Research
.
BibTeX Format
@article{paper1657,
title = { A Library for Learning Neural Operators },
author = {
Kamyar Azizzadenesheli
and Jean Kossaifi
and Anima Anandkumar
and Nikola Kovachki
and Zongyi Li
and David Pitt
and Miguel Liu-Schiaffini
and Robert J. George
and Boris Bonev
and Julius Berner
and Valentin Duruisseaux
},
journal = { Journal of Machine Learning Research },
url = { https://www.jmlr.org/papers/v27/26-0434.html }
}