Ergodic theory, Dynamic Mode Decomposition and Computation of Spectral Properties of the Koopman operator

TL;DR

This paper proves the convergence of Dynamic Mode Decomposition algorithms for computing Koopman operator spectral properties in ergodic systems, enabling accurate analysis of complex dynamical systems from data.

Contribution

It establishes the theoretical convergence of DMD algorithms for Koopman eigenvalues and eigenfunctions in ergodic systems, connecting DMD with proper orthogonal decomposition.

Findings

DMD converges to true Koopman eigenvalues and eigenfunctions with infinite data.

SVD in DMD converges to Proper Orthogonal Decomposition of observables.

Application demonstrated on fluid dynamics and classical dynamical systems.

Abstract

We establish the convergence of a class of numerical algorithms, known as Dynamic Mode Decomposition (DMD), for computation of the eigenvalues and eigenfunctions of the infinite-dimensional Koopman operator. The algorithms act on data coming from observables on a state space, arranged in Hankel-type matrices. The proofs utilize the assumption that the underlying dynamical system is ergodic. This includes the classical measure-preserving systems, as well as systems whose attractors support a physical measure. Our approach relies on the observation that vector projections in DMD can be used to approximate the function projections by the virtue of Birkhoff's ergodic theorem. Using this fact, we show that applying DMD to Hankel data matrices in the limit of infinite-time observations yields the true Koopman eigenfunctions and eigenvalues. We also show that the Singular Value Decomposition, which is the central part of most DMD algorithms, converges to the Proper Orthogonal Decomposition of observables. We use this result to obtain a representation of the dynamics of systems with continuous spectrum based on the lifting of the coordinates to the space of observables. The numerical application of these methods is demonstrated using well-known dynamical systems and examples from computational fluid dynamics.