Deep splitting method for parabolic PDEs

In this paper we introduce a numerical method for nonlinear parabolic PDEs that combines operator splitting with deep learning. It divides the PDE approximation problem into a sequence of separate learning problems. Since the computational graph for each of the subproblems is comparatively small,...

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Bibliographic Details
Main Authors: Beck, Christian, Becker, Sebastian, Cheridito, Patrick, Jentzen, Arnulf, Neufeld, Ariel
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2022
Subjects:
Online Access:https://hdl.handle.net/10356/153744
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Institution: Nanyang Technological University
Language: English