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Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning

by Lorenz Richter, Julius Berner

Year:

2022

Publication:

eprint arXiv:2206.10588

Abstract:

The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations based on associated stochastic differential equations (SDEs), which allow the minimization of corresponding losses using gradient-based optimization methods.

Link:

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Additional Information


Brief introduction of the dida co-author(s) and relevance for dida's ML developments.

About the Co-Author

With an original focus on stochastics and numerics (FU Berlin), the mathematician has been dealing with deep learning algorithms for some time now. Besides his interest in the theory, he has practically solved multiple data science problems in the last 10 years. Lorenz leads the machine learning team.