Pregled bibliografske jedinice broj: 1054683
Koopman Operator Theory for Nonautonomous and Stochastic Systems
Koopman Operator Theory for Nonautonomous and Stochastic Systems // The Koopman Operator in Systems and Control / Mauroy, Alexandre ; Mezic, Igor ; Susuki, Yoshihiko (ur.).
Basel: Springer, 2020. str. 131-160 doi:10.1007/978-3-030-35713-9
CROSBI ID: 1054683 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Koopman Operator Theory for Nonautonomous and Stochastic Systems
Autori
Maćešić, Senka ; Črnjarić-Žic, Nelida
Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, ostalo
Knjiga
The Koopman Operator in Systems and Control
Urednik/ci
Mauroy, Alexandre ; Mezic, Igor ; Susuki, Yoshihiko
Izdavač
Springer
Grad
Basel
Godina
2020
Raspon stranica
131-160
ISBN
978-3-030-35712-2
ISSN
0170-8643
Ključne riječi
Koopman operator, nonautonomous systems, random dynamical system, stochastic Koopman operator, DMD algorithm, Arnoldi type algorithm
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Temeljne tehničke znanosti
POVEZANOST RADA
Ustanove:
Tehnički fakultet, Rijeka,
Sveučilište u Rijeci
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Book Citation Index - Science (BKCI-S)
- Scopus