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An overview on deep learning-based approximation methods for partial differential equations
[article]
2022
arXiv
pre-print
It is one of the most challenging problems in applied mathematics to approximatively solve high-dimensional partial differential equations (PDEs). Recently, several deep learning-based 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
arXiv:2012.12348v3
fatcat:6tt4izmltnalvbqgujirwtk6py