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• W3099504209 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" @default.
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• W3099504209 date "2014-11-01" @default.
• W3099504209 modified "2023-10-17" @default.
• W3099504209 title "Dynamic mode decomposition for large and streaming datasets" @default.
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• W3099504209 doi "https://doi.org/10.1063/1.4901016" @default.
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