Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning
von Lorenz Richter, Julius Berner
Jahr:
2022
Publikation:
eprint arXiv:2206.10588
Abstrakt:
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.
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Brief introduction of the dida co-author(s) and relevance for dida's ML developments.
Dr. Lorenz Richter
Aus der Stochastik und Numerik kommend (FU Berlin), beschäftigt sich der Mathematiker seit einigen Jahren mit Deep-Learning-Algorithmen. Neben seinem Faible für die Theorie hat er in den letzten 10 Jahren diverse Data Science-Probleme praktisch gelöst. Lorenz leitet das Machine-Learning-Team.