Dynamic mode decomposition of noisy flow data

TL;DR

This paper introduces a flexible optimization-based extension to Dynamic Mode Decomposition (DMD) that enhances robustness and accuracy in analyzing noisy fluid flow data, effectively separating coherent dynamics from noise.

Contribution

The paper presents a novel optimization approach that improves DMD's robustness to noise and accurately identifies flow dynamics and noise simultaneously.

Findings

The method shows strong noise robustness in laminar flow data.

It achieves high accuracy in identifying flow structures.

The approach outperforms traditional DMD in noisy conditions.

Abstract

Dynamic mode decomposition (DMD) is a popular approach to analyzing and modeling fluid flows. In practice, datasets are almost always corrupted to some degree by noise. The vanilla DMD is highly noise-sensitive, which is why many algorithmic extensions for improved robustness exist. We introduce a flexible optimization approach that merges available ideas for improved accuracy and robustness. The approach simultaneously identifies coherent dynamics and noise in the data. In tests on the laminar flow past a cylinder, the method displays strong noise robustness and high levels of accuracy.