A Library for Learning Neural Operators

محفوظ في:

التفاصيل البيبلوغرافية
الحاوية / القاعدة:	arXiv.org (Dec 17, 2024), p. n/a
المؤلف الرئيسي:	Kossaifi, Jean
مؤلفون آخرون:	Kovachki, Nikola, Li, Zongyi, Pitt, David, Liu-Schiaffini, Miguel, George, Robert Joseph, Bonev, Boris, Azizzadenesheli, Kamyar, Berner, Julius, Anandkumar, Anima
منشور في:	Cornell University Library, arXiv.org
الموضوعات:	Learning curves Python Function space Convergence Neural networks Operators (mathematics) Discretization Open source software Euclidean geometry
الوصول للمادة أونلاين:	Citation/Abstract Full text outside of ProQuest
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

الوصف
مستخلص:	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.
تدمد:	2331-8422
المصدر:	Engineering Database