Towards an Adaptive Dynamic Mode Decomposition

被引：0
|
作者：
Murshed, Mohammad N. [1 ]
Uddin, M. Monir [1 ]
机构：
[1] North South Univ, Dept Math & Phys, Dhaka 1229, Bangladesh
来源：
RESULTS IN CONTROL AND OPTIMIZATION | 2022年 / 6卷
关键词：
Dynamic mode decomposition; Time delay coordinates; Random projection; Discrete fourier transform; Augmented Lagrange multiplier method; Double gyre; Hidden dynamics; Turbulent channel flow; TURBULENCE;
D O I：
10.1016/j.rico.2021.100076
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Dynamic Mode Decomposition (DMD) is a tool that creates an approximate model from spatiotemporal data. We have developed an architecture of this tool that will adapt to the data from a given problem by leveraging time delay coordinates, projections, and robust principal component analysis. Our scheme which we call Adaptive Dynamic Mode Decomposition (ADMD) can be used in its exact form or the user may even utilize parts of the scheme for generating a DMD model that is more accurate and reliable compared to the one given by standard DMD. ADMD is demonstrated on several datasets of varying complexities and its performance appears to be promising.
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页数：9
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