Solving high-dimensional parabolic PDEs using the tensor train format
von Lorenz Richter, Leon Sallandt, Nikolas Nüsken
Jahr:
2021
Publikation:
eprint arXiv:2102.11830
Abstrakt:
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:
Read the paperAdditional 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.