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

TL;DR

This survey reviews various noise-correcting tools for Dynamic Mode Decomposition, analyzing their effectiveness on datasets with different noise levels and their impact on model accuracy.

Contribution

It provides a comprehensive overview of data-filtering methods for DMD and evaluates their performance across different noise conditions.

Findings

Filtering methods improve DMD accuracy with noisy data

Higher SNR leads to better DMD model fidelity

Different filtering techniques vary in effectiveness depending on data complexity

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.