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On Convergence of Extended Dynamic Mode Decomposition to the Koopman Operator

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TLDR
An analytic version of the EDMD algorithm is proposed which, under some assumptions, allows one to construct the Koopman operator directly, without the use of sampling, and convergence of the predictions of future values of a given observable over any finite time horizon is implied.
Abstract
Extended Dynamic Mode Decomposition (EDMD) is an algorithm that approximates the action of the Koopman operator on an $N$-dimensional subspace of the space of observables by sampling at $M$ points in the state space. Assuming that the samples are drawn either independently or ergodically from some measure $\mu$, it was shown that, in the limit as $M\rightarrow\infty$, the EDMD operator $\mathcal{K}_{N,M}$ converges to $\mathcal{K}_N$, where $\mathcal{K}_N$ is the $L_2(\mu)$-orthogonal projection of the action of the Koopman operator on the finite-dimensional subspace of observables. In this work, we show that, as $N \rightarrow \infty$, the operator $\mathcal{K}_N$ converges in the strong operator topology to the Koopman operator. This in particular implies convergence of the predictions of future values of a given observable over any finite time horizon, a fact important for practical applications such as forecasting, estimation and control. In addition, we show that accumulation points of the spectra of $\mathcal{K}_N$ correspond to the eigenvalues of the Koopman operator with the associated eigenfunctions converging weakly to an eigenfunction of the Koopman operator, provided that the weak limit of eigenfunctions is nonzero. As a by-product, we propose an analytic version of the EDMD algorithm which, under some assumptions, allows one to construct $\mathcal{K}_N$ directly, without the use of sampling. Finally, under additional assumptions, we analyze convergence of $\mathcal{K}_{N,N}$ (i.e., $M=N$), proving convergence, along a subsequence, to weak eigenfunctions (or eigendistributions) related to the eigenmeasures of the Perron-Frobenius operator. No assumptions on the observables belonging to a finite-dimensional invariant subspace of the Koopman operator are required throughout.

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Citations
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Dynamic Mode Decomposition and Its Variants

TL;DR: Dynamic mode decomposition (DMD) is a factorization and dimensionality reduction technique for data sequences as discussed by the authors , which has been applied to numerical and experimental data sequences taken from simple to complex fluid systems.
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Modern Koopman Theory for Dynamical Systems

- 01 May 2022 - 
TL;DR: Koopman spectral theory has emerged as a dominant perspective over the past decade, in which nonlinear dynamics are represented in terms of an infinite-dimensional linear operator acting on the space of all possible measurement functions of the system as mentioned in this paper .
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References
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Journal ArticleDOI

Dynamic mode decomposition of numerical and experimental data

TL;DR: In this article, a method is introduced that is able to extract dynamic information from flow fields that are either generated by a (direct) numerical simulation or visualized/measured in a physical experiment.
Journal ArticleDOI

Spectral analysis of nonlinear flows

TL;DR: In this article, a technique for describing the global behaviour of complex nonlinear flows by decomposing the flow into modes determined from spectral analysis of the Koopman operator, an infinite-dimensional linear operator associated with the full nonlinear system, is presented.
Journal ArticleDOI

A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

TL;DR: In this paper, the authors presented a data-driven method for approximating the leading eigenvalues, eigenfunctions, and modes of the Koopman operator, which requires a data set of snapshot pairs and a dictionary of scalar observables, but does not require explicit governing equations or interaction with a black box integrator.
Journal ArticleDOI

On dynamic mode decomposition: Theory and applications

TL;DR: In this paper, the authors define dynamic mode decomposition (DMD) as the eigendecomposition of an approximating linear operator, and propose sampling strategies that increase computational efficiency and mitigate the effects of noise.
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