Data-Driven Spectral Analysis Through Pseudo-Resolvent Koopman Operator in Dynamical Systems

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Hauptverfasser: Xu, Yuanchao, Sakata, Itsushi, Ishikawa, Isao
Format: Preprint
Veröffentlicht: 2025
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author Xu, Yuanchao
Sakata, Itsushi
Ishikawa, Isao
author_facet Xu, Yuanchao
Sakata, Itsushi
Ishikawa, Isao
contents We present a data-driven method for spectral analysis of the Koopman operator based on direct construction of the pseudo-resolvent from time-series data. Finite-dimensional approximation of the Koopman operator, such as those obtained from Extended Dynamic Mode Decomposition, are known to suffer from spectral pollution. To address this issue, we construct the pseudo-resolvent operator using the Sherman-Morrison-Woodbury identity whose norm serves as a spectral indicator, and pseudoeigenfunctions are extracted as directions of maximal amplification. We establish convergence of the approximate spectrum to the true spectrum in the Hausdorff metric for isolated eigenvalues, with preservation of algebraic multiplicities, and derive error bounds for eigenvalue approximation. Numerical experiments on pendulum, Lorenz, and coupled oscillator systems demonstrate that the method effectively suppresses spectral pollution and resolves closely spaced spectral components.
format Preprint
id arxiv_https___arxiv_org_abs_2512_24953
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Data-Driven Spectral Analysis Through Pseudo-Resolvent Koopman Operator in Dynamical Systems
Xu, Yuanchao
Sakata, Itsushi
Ishikawa, Isao
Dynamical Systems
Chaotic Dynamics
We present a data-driven method for spectral analysis of the Koopman operator based on direct construction of the pseudo-resolvent from time-series data. Finite-dimensional approximation of the Koopman operator, such as those obtained from Extended Dynamic Mode Decomposition, are known to suffer from spectral pollution. To address this issue, we construct the pseudo-resolvent operator using the Sherman-Morrison-Woodbury identity whose norm serves as a spectral indicator, and pseudoeigenfunctions are extracted as directions of maximal amplification. We establish convergence of the approximate spectrum to the true spectrum in the Hausdorff metric for isolated eigenvalues, with preservation of algebraic multiplicities, and derive error bounds for eigenvalue approximation. Numerical experiments on pendulum, Lorenz, and coupled oscillator systems demonstrate that the method effectively suppresses spectral pollution and resolves closely spaced spectral components.
title Data-Driven Spectral Analysis Through Pseudo-Resolvent Koopman Operator in Dynamical Systems
topic Dynamical Systems
Chaotic Dynamics
url https://arxiv.org/abs/2512.24953