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Mobility-induced persistent chimera states.

Gabriela Petrungaro1,2, Koichiro Uriu3, Luis G Morelli1,2,4

  • 1Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA)-CONICET-Partner Institute of the Max Planck Society, Polo Científico Tecnológico, Godoy Cruz 2390, Buenos Aires C1425FQD, Argentina.

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Summary
This summary is machine-generated.

Mobile coupled oscillators exhibit dynamic chimera states where ordered and disordered domains coexist. These complex patterns persist with intermediate mobility, offering insights into systems of communicating agents.

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Area of Science:

  • Complex systems
  • Nonlinear dynamics
  • Network science

Background:

  • Coupled oscillator systems are fundamental to understanding emergent behavior in nature and technology.
  • The influence of mobility and coupling delays on collective dynamics remains an active area of research.

Purpose of the Study:

  • To investigate the impact of mobility and coupling delays on the dynamics of locally coupled identical oscillators.
  • To identify and characterize novel chimera states in mobile oscillator networks.

Main Methods:

  • Simulations of coupled identical oscillators with varying mobility and coupling delays.
  • Analysis of domain formation, coherence, and stability of emergent states.
  • Investigation of underlying mechanisms driving chimera state formation across different mobility regimes.

Main Results:

  • Discovery of diverse chimera states characterized by coexisting coherent (in-phase, antiphase) and incoherent domains.
  • Demonstration that these dynamic chimera states can be long-lived for intermediate mobility values.
  • Elucidation of mechanisms responsible for chimera formation in distinct mobility regimes.

Conclusions:

  • Mobile coupled oscillators can exhibit complex, persistent chimera states influenced by mobility and delays.
  • Findings provide a deeper understanding of collective behavior in systems with mobile, communicating agents.
  • Results have potential implications for designing and analyzing natural and artificial systems with distributed agents.