Dynamic mode decomposition as an analysis tool for time-dependent partial differential equations

Abstract

The time-dependent fields obtained by solving partial differential equations in two and more dimensions quickly overwhelm the analytical capabilities of the human brain. A meaningful insight into the temporal behaviour can be obtained by using scalar reductions, which, however, come with a loss of spatial detail. Dynamic Mode Decomposition is a data-driven analysis method that solves this problem by identifying oscillating spatial structures and their corresponding frequencies. This paper presents the algorithm and provides a physical interpretation of the results by applying the decomposition method to a series of increasingly complex examples.

6 pages, 8 figures

References in corpus (2)

• Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video
• Medusa: A C++ Library for solving PDEs using Strong Form Mesh-Free methods

Cited by in corpus (1)

• Nonintrusive reduced order modeling of convective Boussinesq flows