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arXiv · 2103.02338

A survey of the noise-correcting tools for Dynamic Mode Decomposition

Moajjem H. Chowdhury·Nazmul Islam Shuzan·Mohammad N. Murshed·Sanwar Alam·M. Monir Uddin·Zarin Subah
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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.

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Moajjem H. Chowdhury, Nazmul Islam Shuzan, Mohammad N. Murshed, Sanwar Alam, M. Monir Uddin, Zarin Subah. 2021-03-03. A survey of the noise-correcting tools for Dynamic Mode Decomposition. https://arxiv.org/abs/2103.02338

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