Imagen de Portada

A Library for Learning Neural Operators

Guardado en:
Publicado en:arXiv.org (Dec 17, 2024), p. n/a
Autor principal: Kossaifi, Jean
Otros Autores: Kovachki, Nikola, Li, Zongyi, Pitt, David, Liu-Schiaffini, Miguel, George, Robert Joseph, Bonev, Boris, Azizzadenesheli, Kamyar, Berner, Julius, Anandkumar, Anima
Publicado:
Cornell University Library, arXiv.org
Materias:
Learning curves
Python
Function space
Convergence
Neural networks
Operators (mathematics)
Discretization
Open source software
Euclidean geometry
Acceso en línea:Citation/Abstract
Full text outside of ProQuest
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Resumen: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
Fuente:Engineering Database