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,...
Saved in:
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 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
An efficient Monte Carlo scheme for Zakai equations
by: Beck, Christian, et al.
Published: (2024) -
Order of convergence of splitting schemes for both deterministic and stochastic nonlinear Schrödinger equations
by: Liu, J.
Published: (2014) -
Complete blow-up for a semilinear parabolic equation
by: Panumart Sawangtong
Published: (2009) -
Nonlocal fully nonlinear parabolic differential equations arising in time-inconsistent problems
by: Lei, Qian, et al.
Published: (2023) -
Multiscale methods : averaging and homogenization
by: Pavliotis, Grigorios A., et al.
Published: (2017)