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Recurrent chaotic clustering and slow chaos in adaptive networks

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Author(s):
Rolim Sales, Matheus ; Yanchuk, Serhiy ; Kurths, Juergen
Total Authors: 3
Document type: Journal article
Source: Chaos; v. 34, n. 6, p. 8-pg., 2024-06-01.
Abstract

Adaptive dynamical networks are network systems in which the structure co-evolves and interacts with the dynamical state of the nodes. We study an adaptive dynamical network in which the structure changes on a slower time scale relative to the fast dynamics of the nodes. We identify a phenomenon we refer to as recurrent adaptive chaotic clustering (RACC), in which chaos is observed on a slow time scale, while the fast time scale exhibits regular dynamics. Such slow chaos is further characterized by long (relative to the fast time scale) regimes of frequency clusters or frequency-synchronized dynamics, interrupted by fast jumps between these regimes. We also determine parameter values where the time intervals between jumps are chaotic and show that such a state is robust to changes in parameters and initial conditions. (AU)

FAPESP's process: 23/08698-9 - Transport processes in Hamiltonian systems
Grantee:Matheus Rolim Sales
Support Opportunities: Scholarships in Brazil - Post-Doctoral