Work

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

Year: 2022

Type: conference-paper

Abstract: The time-dependent fields obtained by solving par-tial 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.

Source: International Conference on Smart and Sustainable Technologies (SpliTech)

DOI: 10.23919/splitech55088.2022.9854243

Authors: Miha Rot, Martin Horvat, Gregor Kosec

Institutions: Jožef Stefan Institute, Jožef Stefan International Postgraduate School, University of Ljubljana

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funder: Javna Agencija za Raziskovalno Dejavnost RS

Award: Scientific Computing