A Library for Learning Neural Operators

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Xehetasun bibliografikoak
Argitaratua izan da:	arXiv.org (Dec 17, 2024), p. n/a
Egile nagusia:	Kossaifi, Jean
Beste egile batzuk:	Kovachki, Nikola, Li, Zongyi, Pitt, David, Liu-Schiaffini, Miguel, George, Robert Joseph, Bonev, Boris, Azizzadenesheli, Kamyar, Berner, Julius, Anandkumar, Anima
Argitaratua:	Cornell University Library, arXiv.org
Gaiak:	Learning curves Python Function space Convergence Neural networks Operators (mathematics) Discretization Open source software Euclidean geometry
Sarrera elektronikoa:	Citation/Abstract Full text outside of ProQuest
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!

Deskribapena
Laburpena:	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.
ISSN:	2331-8422
Baliabidea:	Engineering Database