Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods
Joint Authors
Le Clainche, Soledad
Vega, José M.
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-12
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
This article presents a review on two methods based on dynamic mode decomposition and its multiple applications, focusing on higher order dynamic mode decomposition (which provides a purely temporal Fourier-like decomposition) and spatiotemporal Koopman decomposition (which gives a spatiotemporal Fourier-like decomposition).
These methods are purely data-driven, using either numerical or experimental data, and permit reconstructing the given data and identifying the temporal growth rates and frequencies involved in the dynamics and the spatial growth rates and wavenumbers in the case of the spatiotemporal Koopman decomposition.
Thus, they may be used to either identify and extrapolate the dynamics from transient behavior to permanent dynamics or construct efficient, purely data-driven reduced order models.
American Psychological Association (APA)
Le Clainche, Soledad& Vega, José M.. 2018. Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods. Complexity،Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1135538
Modern Language Association (MLA)
Le Clainche, Soledad& Vega, José M.. Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods. Complexity No. 2018 (2018), pp.1-21.
https://search.emarefa.net/detail/BIM-1135538
American Medical Association (AMA)
Le Clainche, Soledad& Vega, José M.. Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods. Complexity. 2018. Vol. 2018, no. 2018, pp.1-21.
https://search.emarefa.net/detail/BIM-1135538
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135538