A Library for Learning Neural Operators
A Library for Learning Neural Operators
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.
Jean Kossaifi、Nikola Kovachki、Zongyi Li、David Pitt、Miguel Liu-Schiaffini、Valentin Duruisseaux、Robert Joseph George、Boris Bonev、Kamyar Azizzadenesheli、Julius Berner、Anima Anandkumar
计算技术、计算机技术
Jean Kossaifi,Nikola Kovachki,Zongyi Li,David Pitt,Miguel Liu-Schiaffini,Valentin Duruisseaux,Robert Joseph George,Boris Bonev,Kamyar Azizzadenesheli,Julius Berner,Anima Anandkumar.A Library for Learning Neural Operators[EB/OL].(2025-06-29)[2025-07-21].https://arxiv.org/abs/2412.10354.点此复制
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