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Interpolating between BSDEs and PINNs: deep learning for elliptic and parabolic boundary value problems

by Nikolas Nüsken, Lorenz Richter




eprint arXiv:2112.03749


Solving high-dimensional partial differential equations is a recurrent challenge in economics, science and engineering. In recent years, a great number of computational approaches have been developed, most of them relying on a combination of Monte Carlo sampling and deep learning based approximation.


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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.