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We formulate a low-storage method for performing dynamic mode decompositionthat can be updated inexpensively as new data become available; thisformulation allows dynamical information to be extracted from large datasetsand data streams. We present two algorithms: the first is mathematicallyequivalent to a standard "batch-processed" formulation; the second introduces acompression step that maintains computational efficiency, while enhancing theability to isolate pertinent dynamical information from noisy measurements.Both algorithms reliably capture dominant fluid dynamic behaviors, asdemonstrated on cylinder wake data collected from both direct numericalsimulations and particle image velocimetry experiments

Dynamic Mode Decomposition for Large and Streaming Datasets