Reduced-order modelling based on Koopman operator theory

TL;DR

This paper introduces a reduced-order modeling approach in fluid dynamics using Koopman operator theory, focusing on efficiently identifying impactful modes to simplify complex numerical simulations.

Contribution

It presents a novel strategy for selecting the most impactful Koopman modes to improve reduced-order models in fluid dynamics.

Findings

Effective identification of dominant Koopman modes

Reduced computational complexity in fluid simulations

Enhanced accuracy of reduced-order models

Abstract

The present study focuses on a subject of significant interest in fluid dynamics: the identification of a model with decreased computational complexity from numerical code output using Koopman operator theory. A reduced-order modelling method that incorporates a novel strategy for identifying the most impactful Koopman modes was used to numerically approximate the Koopman composition operator.