Model Order Reduction for Stochastic Delay Equations With Error Bounds

Jahr:

2020

Publikation:

eprint arXiv:2008.12288

Abstrakt:

We analyze a structure-preserving model order reduction technique for delay and stochastic delay equations based on the balanced truncation method and provide a system theoretic interpretation. Transferring error bounds based on Hankel operators to delay systems, we find error estimates for the difference between the dynamics of the full and reduced model.

Link:

Read the paper

Additional Information

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.