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Journal of Scientific Computing

, Volume 79, Issue 3, pp 1667–1712 | Cite as

Machine Learning for Semi Linear PDEs

  • Quentin Chan-Wai-Nam
  • Joseph Mikael
  • Xavier WarinEmail author
Article

Abstract

Recent machine learning algorithms dedicated to solving semi-linear PDEs are improved by using different neural network architectures and different parameterizations. These algorithms are compared to a new one that solves a fixed point problem by using deep learning techniques. This new algorithm appears to be competitive in terms of accuracy with the best existing algorithms.

Keywords

Semilinear PDEs Monte-Carlo methods Machine learning Deep learning 

Notes

Acknowledgements

The authors would like to thank Simon Fécamp for useful discussions and technical advices.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.EDF Lab Paris-SaclayPalaiseauFrance
  2. 2.Laboratoire de Finance des Marchés de l’EnergieFiMEChatouFrance

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