• Corpus ID: 229363798

# An overview on deep learning-based approximation methods for partial differential equations

@article{Beck2020AnOO,
title={An overview on deep learning-based approximation methods for partial differential equations},
author={Christian Beck and Martin Hutzenthaler and Arnulf Jentzen and Benno Kuckuck},
journal={ArXiv},
year={2020},
volume={abs/2012.12348}
}
• Published 22 December 2020
• Computer Science
• ArXiv
It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learningbased approximation algorithms for attacking this problem have been proposed and tested numerically on a number of examples of high-dimensional PDEs. This has given rise to a lively field of research in which deep learning-based methods and related Monte Carlo methods are applied to the approximation of high-dimensional…
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