Group-Convolutional Extended Dynamic Mode Decomposition

TL;DR

This paper introduces a group-convolutional approach to enhance the efficiency and accuracy of Extended Dynamic Mode Decomposition (EDMD) for analyzing equivariant dynamical systems, leveraging symmetry properties to improve learning and prediction.

Contribution

It demonstrates that under symmetry assumptions, the EDMD matrix is equivariant, enabling data-efficient learning and fast eigenfunction approximation through group convolutions and Fourier transforms.

Findings

Equivariant EDMD matrices can be represented via group convolutions.

The approach improves data efficiency and prediction speed for symmetric systems.

Numerical experiments validate the method on nonlinear PDEs like Kuramoto-Sivashinsky and spiraling wave systems.

Abstract

This paper explores the integration of symmetries into the Koopman-operator framework for the analysis and efficient learning of equivariant dynamical systems using a group-convolutional approach. Approximating the Koopman operator by finite-dimensional surrogates, e.g., via extended dynamic mode decomposition (EDMD), is challenging for high-dimensional systems due to computational constraints. To tackle this problem with a particular focus on EDMD, we demonstrate -- under suitable equivarance assumptions on the system and the observables -- that the optimal EDMD matrix is equivariant. That is, its action on states can be described by group convolutions and the generalized Fourier transform. We show that this structural property has many advantages for equivariant systems, in particular, that it allows for data-efficient learning, fast predictions and fast eigenfunction approximations. We conduct numerical experiments on the Kuramoto-Sivashinsky equation and a $\lambda$-$\omega$ spiraling wave system, both nonlinear partial differential equations, providing evidence of the effectiveness of this approach, and highlighting its potential for broader applications in dynamical systems analysis.