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ISOKANN: Invariant subspaces of Koopman operators learned by a neural network

  • The problem of determining the rate of rare events in dynamical systems is quite well-known but still difficult to solve. Recent attempts to overcome this problem exploit the fact that dynamic systems can be represented by a linear operator, such as the Koopman operator. Mathematically, the rare event problem comes down to the difficulty in finding invariant subspaces of these Koopman operators K. In this article, we describe a method to learn basis functions of invariant subspaces using an artificial neural Network.

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Metadaten
Author:Robert Julian Rabben, Sourav Ray, Marcus Weber
Document Type:Article
Parent Title (English):The Journal of Chemical Physics
Volume:153
Issue:11
First Page:114109
Year of first publication:2020
DOI:https://doi.org/10.1063/5.0015132
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