Classification of Chimera States via Fourier Analysis and Unsupervised Learning

TL;DR

This paper introduces a novel method combining Fourier analysis and unsupervised learning to classify and distinguish different types of chimera states in coupled oscillator systems.

Contribution

It presents a new approach that overcomes previous limitations by accurately identifying and classifying chimera states using signal features and clustering techniques.

Findings

Successfully identified regions with chimera states in parameter space.

Distinguished between different types of chimera states.

Applied method to Rayleigh oscillator networks with rich dynamics.

Abstract

Chimera states are among the most intriguing phenomena in nonlinear dynamics, characterized by the coexistence of coherent and incoherent behavior in systems of coupled identical oscillators. Many methods have been proposed to detect chimera states and to distinguish their different types. However, such methods often suffer from important limitations that prevent sufficiently precise classification. In this work, we overcome the issue by considering a method based on Fourier analysis to determine key signal characteristics such as amplitude, phase, and frequency, jointly with an unsupervised clustering step acting on normalized total variations, measures of local spatial changes of the above-mentioned dynamical features. The proposed method allows us to identify regions in parameter space returning chimera states, but also to further distinguish between the different types. The method is applied to a network of Rayleigh oscillators, which has been shown to exhibit a rich variety of dynamical patterns.