Koopman Operator Theory for Nonautonomous and Stochastic Systems
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In practice, the dynamics of open systems subject to time-dependent or random forcing is much more present than the dynamics of autonomous systems. Therefore, extension of the Koopman operator theory and applications to such systems is of great importance. At the same time, it brings a new viewpoint to the existing theory of nonautonomous as well as random dynamical systems, particularly, with application of Koopman-based data-driven algorithms. In this chapter, we first review the nonautonomous Koopman operator family based on the two standard nonautonomous dynamical system definitions: skew product and process. Then, we state basic properties of the operator and compare performance of the DMD and Arnoldi-type algorithms in the context of both definitions. In the case of the random dynamical systems (RDS), we introduce the associated stochastic Koopman operator family. We show that when RDS is Markovian, this family satisfies semigroup property and we present some properties for RDS generated by the stochastic differential equations. Finally, we discuss data-driven algorithms in the stochastic framework and illustrate their performance on numerical examples.
This research has been supported by the DARPA Contract HR0011-16-C-0116 “On A Data-Driven, Operator-Theoretic Framework for Space–Time Analysis of Process Dynamics”. S.M. and N.C-Z. are grateful to Prof. Igor Mezić for helpful mathematical discussions and comments on the manuscript.
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