A survey of the noise-correcting tools for Dynamic Mode Decomposition
Optimization and Control
2021-03-04 v1
Abstract
Dynamic Mode Decomposition (DMD) is a data-driven modeling tool that generates a model from spatio-temporal data. The data needs to be as clean as possible for DMD to come up with a faithful model. We review a few data-filtering methods to be integrated with DMD and test them on datasets of varying complexity. The impact of SNR on these methods and the error variation in the DMD model due to each method are observed and discussed.
Keywords
Cite
@article{arxiv.2103.02338,
title = {A survey of the noise-correcting tools for Dynamic Mode Decomposition},
author = {Moajjem H. Chowdhury and Nazmul Islam Shuzan and Mohammad N. Murshed and Sanwar Alam and M. Monir Uddin and Zarin Subah},
journal= {arXiv preprint arXiv:2103.02338},
year = {2021}
}
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13 pages