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Effects of topological characteristics on rhythmic states of the D-dimensional Kuramoto model in complex networks.
Ling, Xiang; Ju, Wen-Bin; Guo, Ning; Zhu, Kong-Jin; Wu, Chao-Yun; Hao, Qing-Yi.
  • Ling X; School of Automotive and Transportation Engineering, Hefei University of Technology, 230009 Hefei, People's Republic of China.
  • Ju WB; School of Automotive and Transportation Engineering, Hefei University of Technology, 230009 Hefei, People's Republic of China.
  • Guo N; School of Automotive and Transportation Engineering, Hefei University of Technology, 230009 Hefei, People's Republic of China.
  • Zhu KJ; School of Automotive and Transportation Engineering, Hefei University of Technology, 230009 Hefei, People's Republic of China.
  • Wu CY; School of Mathematics and Physics, Anqing Normal University, Anqing 246133, People's Republic of China.
  • Hao QY; School of Mathematics and Physics, Anqing Normal University, Anqing 246133, People's Republic of China.
Chaos ; 32(1): 013118, 2022 Jan.
Article en En | MEDLINE | ID: mdl-35105134
ABSTRACT
Synchronization is a ubiquitous phenomenon in engineering and natural ecosystems. While the dynamics of synchronization modeled by the Kuramoto model are commonly studied in two dimensions and the state of dynamic units is characterized by a scalar angle variable, we studied the Kuramoto model generalized to D dimensions in the framework of a complex network and utilized the local synchronous order parameter between the agent and its neighbors as the controllable variable to adjust the coupling strength. Here, we reported that average connectivity of networks affects the time-dependent, rhythmic, cyclic state. Importantly, we found that the level of heterogeneity of networks governs the rhythmic state in the transition process. The analytical treatment for observed scenarios in a D-dimensional Kuramoto model at D=3 was provided. These results offered a platform for a better understanding of time-dependent swarming and flocking dynamics in nature.

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Año: 2022 Tipo del documento: Article