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Dynamic mode decomposition for large and streaming datasets

Author(s): Hemati, Maziar S; Williams, Matthew O; Rowley, Clarence W

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Abstract: We formulate a low-storage method for performing dynamic mode decomposition that can be updated inexpensively as new data become available; this formulation allows dynamical information to be extracted from large datasets and data streams. We present two algorithms: the first is mathematically equivalent to a standard “batch-processed” formulation; the second introduces a compression step that maintains computational efficiency, while enhancing the ability to isolate pertinent dynamical information from noisy measurements. Both algorithms reliably capture dominant fluid dynamic behaviors, as demonstrated on cylinder wake data collected from both direct numerical simulations and particle image velocimetry experiments.
Publication Date: Nov-2014
Citation: Hemati, Maziar S, Williams, Matthew O, Rowley, Clarence W. "Dynamic mode decomposition for large and streaming datasets" Physics of Fluids, (11), 26, 111701 - 111701, doi:10.1063/1.4901016
DOI: doi:10.1063/1.4901016
ISSN: 1070-6631
EISSN: 1089-7666
Pages: 111701-1 - 111701-6
Type of Material: Journal Article
Journal/Proceeding Title: Physics of Fluids
Version: This is the publisher’s version of the article (version of record). All rights reserved to the publisher. Please refer to the publisher's site for terms of use.

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