Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)

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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics - number of accesses to the document
Author:Robert Julian Rabben, Sourav Ray, Marcus Weber
Document Type:Article
Parent Title (English):The Journal of Chemical Physics
First Page:114109
Year of first publication:2020
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.