A Library for Learning Neural Operators Article Swipe

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Computer science Artificial intelligence Artificial neural network

Jean Kossaifi , Nikola Kovachki , Zongyi Li , David Pitt , Miguel Liu-Schiaffini , Robert Joseph George , Boris Bonev , Kamyar Azizzadenesheli , Julius Berner , Anima Anandkumar ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2412.10354 · OA: W4405434114

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.