Subspace dynamic mode decomposition for stochastic Koopman analysis

Physical Review. E
Naoya TakeishiTakehisa Yairi

Abstract

The analysis of nonlinear dynamical systems based on the Koopman operator is attracting attention in various applications. Dynamic mode decomposition (DMD) is a data-driven algorithm for Koopman spectral analysis, and several variants with a wide range of applications have been proposed. However, popular implementations of DMD suffer from observation noise on random dynamical systems and generate inaccurate estimation of the spectra of the stochastic Koopman operator. In this paper, we propose subspace DMD as an algorithm for the Koopman analysis of random dynamical systems with observation noise. Subspace DMD first computes the orthogonal projection of future snapshots to the space of past snapshots and then estimates the spectra of a linear model, and its output converges to the spectra of the stochastic Koopman operator under standard assumptions. We investigate the empirical performance of subspace DMD with several dynamical systems and show its utility for the Koopman analysis of random dynamical systems.

References

May 1, 1931·Proceedings of the National Academy of Sciences of the United States of America·B O Koopman
Jan 3, 2013·Chaos·Marko BudisićIgor Mezić
May 3, 2014·Medical Physics·George C BourantasGeorge C Nikiforidis
Mar 4, 2015·International Health·Joshua L Proctor, Philip A Eckhoff
Nov 4, 2015·Journal of Neuroscience Methods·Bingni W BruntonJ Nathan Kutz
Apr 2, 2016·Proceedings of the National Academy of Sciences of the United States of America·Steven L BruntonJ Nathan Kutz
Jun 1, 2017·Nature Communications·Steven L BruntonJ Nathan Kutz

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