Dynamic mode decomposition for large and streaming datasets
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 01, 2014
- Source ID
- 10.1063/1.4901016
Entities
People
- Clarence W. Rowley
- Matthew O. Williams
- Maziar S. Hemati
Organizations
- Air Force Office of Scientific Research
- National Science Foundation
- Princeton University