Solving high-dimensional parabolic PDEs using the tensor train format
by Lorenz Richter, Leon Sallandt, Nikolas Nüsken
Year:
2021
Publication:
eprint arXiv:2102.11830
Abstract:
High-dimensional partial differential equations (PDEs) are ubiquitous in economics, science and engineering. However, their numerical treatment poses formidable challenges since traditional grid-based methods tend to be frustrated by the curse of dimensionality.
Link:
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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.