Three ways to solve partial differential equations with neural networks — A review-Reference-Cited by-同舟云学术

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Three ways to solve partial differential equations with neural networks — A review

Published:2021-05-28 Issue:2 Volume:44 Page:
ISSN:0936-7195
Container-title:GAMM-Mitteilungen
language:en
Short-container-title:GAMM‐Mitteilungen

Author:

Blechschmidt Jan¹,Ernst Oliver G.¹

Affiliation:

1. Department of Mathematics TU Chemnitz Saxony Germany

Funder

Bundesministerium für Bildung und Forschung

Publisher

Wiley

Subject

Applied Mathematics,General Physics and Astronomy,General Materials Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/gamm.202100006

Reference183 articles.

1. M.Abadiet al. TensorFlow: Large‐scale machine learning on heterogeneous systems 2015. Software available fromtensorflow.org.

2. A.Bachouchet al. Deep neural networks algorithms for stochastic control problems on finite horizon: Numerical applications arXiv:1812.05916 2020.

3. Spectral and finite difference solutions of the Burgers equation

4. Automatic differentiation in machine learning: A survey;Baydin A. G.;J. Mach. Learn. Res.,2018

5. Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations

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