Dynamic mode decomposition for large and streaming datasets

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 equiv...

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Bibliographic Details
Published inPhysics of fluids (1994) Vol. 26; no. 11
Main Authors Hemati, Maziar S., Williams, Matthew O., Rowley, Clarence W.
Format Journal Article
LanguageEnglish
Published Melville American Institute of Physics 01.11.2014
Subjects
Algorithms
Computer simulation
Computing time
Cylinders
Datasets
Decomposition
Fluid dynamics
Particle image velocimetry
Physics
Velocity measurement
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Summary: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.
ISSN:1070-6631
1089-7666
DOI:10.1063/1.4901016