Investigating the Effectiveness of DMD and its Variants for Complex Data Analysis

The unprecedented availability of data in various fields reinforces the need for more comprehensive and advanced data-driven algorithms to deal with it. The data-driven techniques are able to handle the large volume of heterogeneous data and to extract more valuable information regarding the underlying system. This paper investigates the effectiveness of modern data-driven methods for data from non-linear systems. The effectiveness of dynamic mode decomposition (DMD) and its variants such as rSVD-DMD, randomized DMD (RDMD) and total DMD (TDMD) for dta from nonlinear system is investigated. The data considered for this study are cylindrical fluid flow and neuro recordings.