Data-Driven Model Reduction and Transfer Operator Approximation View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-06

AUTHORS

Stefan Klus, Feliks Nüske, Péter Koltai, Hao Wu, Ioannis Kevrekidis, Christof Schütte, Frank Noé

ABSTRACT

In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues, eigenfunctions, and eigenmodes. The goal is to point out similarities and differences between methods developed independently by the dynamical systems, fluid dynamics, and molecular dynamics communities such as time-lagged independent component analysis, dynamic mode decomposition, and their respective generalizations. As a result, extensions and best practices developed for one particular method can be carried over to other related methods. More... »

PAGES

985-1010

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00332-017-9437-7

DOI

http://dx.doi.org/10.1007/s00332-017-9437-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100163163


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