Self-adaptation of chimera states

Nan Yao, Zi-Gang Huang (Corresponding Author), Hai Peng Ren, Celso Grebogi, Ying-Cheng Lai

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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 controlled generation of chimera states with their coherent component placed in any desired spatial region of the system.
Original languageEnglish
Article number010201(R)
Number of pages6
JournalPhysical Review. E, Statistical, Nonlinear and Soft Matter Physics
Issue number1
Early online date9 Jan 2019
Publication statusPublished - Jan 2019

Bibliographical note

This work was partially supported by the National Natural Science Foundation of China (Grants No. 11647052 and No. 61431012), by the Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 17JK0553), by the Young Talent fund of University Association for Science and Technology in Shaanxi, China (Program No. 20170606), and by the Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2018JQ1010). Z.-G.H acknowledges support of K. C. Wong Education Foundation. Y.-C.L is supported by ONR under Grant No. N00014-16-1-2828.


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