De-biasing the dynamic mode decomposition for applied Koopman spectral analysis of noisy datasets

The dynamic mode decomposition (DMD)—a popular method for performing data-driven Koopman spectral analysis—has gained increased popularity for extracting dynamically meaningful spatiotemporal descriptions of fluid flows from snapshot measurements. Often times, DMD descriptions can be used for predic...

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Veröffentlicht in:	Theoretical and computational fluid dynamics Jg. 31; H. 4; S. 349 - 368
Hauptverfasser:	Hemati, Maziar S., Rowley, Clarence W., Deem, Eric A., Cattafesta, Louis N.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017 Springer Springer Nature B.V
Schlagworte:	Analysis Asymmetry Classical and Continuum Physics Computational fluid dynamics Computational Science and Engineering Decomposition Engineering Engineering Fluid Dynamics Errors Flow (Dynamics) Flow separation Fluid dynamics Fluid flow Fluids Forecasting Frameworks Least squares method Mathematical models Modes Noise Original Article Particle image velocimetry Sensors Spectral analysis Spectral analysis (Signal analysis) Structures Velocity measurement Yields Koopman spectral analysis Sensor noise Data-driven dynamical systems Experimental fluid mechanics Reduced-order model Total least-squares
ISSN:	0935-4964, 1432-2250
Online-Zugang:	Volltext
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