Error bounds for model reduction of feedback-controlled linear stochastic dynamics on Hilbert spaces
von Simon Becker, Carsten Hartmann, Martin Redmann, Lorenz Richter
Jahr:
2019
Publikation:
eprint arXiv:1912.06113
Abstrakt:
We analyze structure-preserving model order reduction methods for Ornstein-Uhlenbeck processes and linear S(P)DEs with multiplicative noise based on balanced truncation. For the first time, we include in this study the analysis of non-zero initial conditions.
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.