Dynamic mode decomposition for large and streaming datasets

被引：202

作者：

Hemati, Maziar S. ^{[1
]}

Williams, Matthew O. ^{[2
]}

Rowley, Clarence W. ^{[1
]}

机构：

[1] Princeton Univ, Dept Mech & Aerosp Engn, Princeton, NJ 08544 USA

[2] Princeton Univ, Program Appl & Computat Math, Princeton, NJ 08544 USA

来源：

PHYSICS OF FLUIDS | 2014年 / 26卷 / 11期

基金：

美国国家科学基金会;

关键词：

Velocity measurement - Computational efficiency - Digital storage - Fluid dynamics;

D O I：

10.1063/1.4901016

中图分类号：

O3 [力学];

学科分类号：

08 ; 0801 ;

摘要：

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. (C) 2014 AIP Publishing LLC.

引用

页数：6