Dynamic mode decomposition for large and streaming datasets

Maziar S. Hemati, Matthew O. Williams, Clarence Worth Rowley

Research output: Contribution to journalArticlepeer-review

100 Scopus citations

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.

Original languageEnglish (US)
Article number111701
JournalPhysics of Fluids
Volume26
Issue number11
DOIs
StatePublished - Nov 4 2014

All Science Journal Classification (ASJC) codes

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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