Dynamic Mode Decomposition: Theory and Data Reconstruction
Numerical Analysis
2022-02-15 v2 Numerical Analysis
Abstract
Dynamic Mode Decomposition (DMD) is a data-driven decomposition technique extracting spatio-temporal patterns of time-dependent phenomena. In this paper, we perform a comprehensive theoretical analysis of various variants of DMD. We provide a systematic advancement of these and examine the interrelations. In addition, several results of each variant are proven. Our main result is the exact reconstruction property. To this end, a new modification of scaling factors is presented and a new concept of an error scaling is introduced to guarantee an error-free reconstruction of the data.
Cite
@article{arxiv.1909.10466,
title = {Dynamic Mode Decomposition: Theory and Data Reconstruction},
author = {Tim Krake and Daniel Weiskopf and Bernhard Eberhardt},
journal= {arXiv preprint arXiv:1909.10466},
year = {2022}
}