Improving control based importance sampling strategies for metastable diffusions via adapted metadynamics
von Enric Ribera Borrell, Jannes Quer, Lorenz Richter, Christof Schütte
Jahr:
2022
Publikation:
eprint arXiv:2206.06628
Abstrakt:
Sampling rare events in metastable dynamical systems is often a computationally expensive task and one needs to resort to enhanced sampling methods such as importance sampling. Since we can formulate the problem of finding optimal importance sampling controls as a stochastic optimization problem, this then brings additional numerical challenges and the convergence of corresponding algorithms might as well suffer from metastabilty.
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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.