| Autor: |
Kossaifi, Jean, Kovachki, Nikola, Li, Zongyi, Pitt, Davit, Liu-Schiaffini, Miguel, George, Robert Joseph, Bonev, Boris, Azizzadenesheli, Kamyar, Berner, Julius, Anandkumar, Anima |
| Rok vydání: |
2024 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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