A Library for Learning Neural Operators

• URL: http://arxiv.org/abs/2412.10354v3
• Date: Wed, 30 Apr 2025 17:23:25 GMT
• Title: A Library for Learning Neural Operators
• Authors: Jean Kossaifi, Nikola Kovachki, Zongyi Li, David Pitt, Miguel Liu-Schiaffini, Robert Joseph George, Boris Bonev, Kamyar Azizzadenesheli, Julius Berner, Valentin Duruisseaux, Anima Anandkumar,
• Abstract summary: We present NeuralOperator, an open-source Python library for operator learning.<n> Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces.<n>Built on top of PyTorch, NeuralOperator provides all the tools for training and deploying neural operator models.
• Score: 75.14579433742178
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Built on top of PyTorch, 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.

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