Self-adaptation of chimera states.


Journal

Physical review. E
ISSN: 2470-0053
Titre abrégé: Phys Rev E
Pays: United States
ID NLM: 101676019

Informations de publication

Date de publication:
Jan 2019
Historique:
received: 24 10 2018
entrez: 21 2 2019
pubmed: 20 2 2019
medline: 20 2 2019
Statut: ppublish

Résumé

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, and have been shown to be robust against random perturbations. How do chimera states achieve their robustness? We uncover a self-adaptation behavior by which, upon a spatially localized perturbation, the coherent component of the chimera state spontaneously drifts to an optimal location as far away from the perturbation as possible, exposing only its incoherent component to the perturbation to minimize the disturbance. A systematic numerical analysis of the evolution of the spatiotemporal pattern of the chimera state towards the optimal stable state reveals an exponential relaxation process independent of the spatial location of the perturbation, implying that its effects can be modeled as restoring and damping forces in a mechanical system and enabling the articulation of a phenomenological model. Not only is the model able to reproduce the numerical results, it can also predict the trajectory of drifting. Our finding is striking as it reveals that, inherently, chimera states possess a kind of "intelligence" in achieving robustness through self-adaptation. The behavior can be exploited for the controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.

Identifiants

pubmed: 30780345
doi: 10.1103/PhysRevE.99.010201
doi:

Types de publication

Journal Article

Langues

eng

Pagination

010201

Auteurs

Nan Yao (N)

Department of Applied Physics, Xi'an University of Technology, Xi'an 710048, China.

Zi-Gang Huang (ZG)

The Key Laboratory of Biomedical Information Engineering of Ministry of Education, National Engineering Research Center of Health Care and Medical Devices, The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China.

Hai-Peng Ren (HP)

Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi'an University of Technology, Xi'an 710048, China.

Celso Grebogi (C)

Institute for Complex Systems and Mathematical Biology, King's College, University of Aberdeen, Aberdeen AB24 3UE, United Kingdom.

Ying-Cheng Lai (YC)

School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
Department of Physics, Arizona State University, Tempe, Arizona 85287, USA.

Classifications MeSH