Enhancing spectral analysis in nonlinear dynamics with pseudoeigenfunctions from continuous spectra

TL;DR

This paper introduces a new method to analyze complex dynamic systems by using pseudoeigenfunctions from continuous spectra, improving on existing techniques like DMD.

Contribution

A clustering-based approach is proposed to analyze pseudoeigenfunctions from continuous spectra, enhancing spectral analysis in nonlinear dynamics.

Findings

The method reveals dynamic patterns previously hidden by traditional DMD analyses.

It provides insights into the complexities of coupled chaotic systems.

The approach is validated using 1D and 2D time series data affected by noise and chaos.

Abstract

The analysis of complex behavior in empirical data poses significant challenges in various scientific and engineering disciplines. Dynamic Mode Decomposition (DMD) is a widely used method to reveal the spectral features of nonlinear dynamical systems without prior knowledge. However, because of its infinite dimensions, analyzing the continuous spectrum resulting from chaos and noise is problematic. We propose a clustering-based method to analyze dynamics represented by pseudoeigenfunctions associated with continuous spectra. This paper describes data-driven algorithms for comparing pseudoeigenfunctions using subspaces. We used the recently proposed Residual Dynamic Mode Decomposition (ResDMD) to approximate spectral properties from the data. To validate the effectiveness of our method, we analyzed 1D signal data affected by thermal noise and 2D-time series of coupled chaotic systems exhibiting generalized synchronization. The results reveal dynamic patterns previously obscured by conventional DMD analyses and provide insights into coupled chaos’s complexities.

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