Multi-population phase oscillator networks with higher-order interactions

被引:13
|
作者
Bick, Christian [1 ,2 ,3 ]
Boehle, Tobias [4 ]
Kuehn, Christian [4 ]
机构
[1] Univ Exeter, Dept Math, Exeter EX4 4QF, Devon, England
[2] Tech Univ Munich, Inst Adv Study, Lichtenbergstr 2, D-85748 Garching, Germany
[3] Vrije Univ Amsterdam, Dept Math, Boelelaan 1111, Amsterdam, Netherlands
[4] Tech Univ Munich, Dept Math M8, Boltzmannstr 3, D-85748 Garching, Germany
基金
英国工程与自然科学研究理事会;
关键词
Stability analysis; Characteristic system; Mean-field; Higher-order interactions; Synchronization; Kuramoto model; KURAMOTO MODEL; HETEROCLINIC DYNAMICS; COUPLED OSCILLATORS; COMMUNITY STRUCTURE; SYNCHRONIZATION; STABILITY; EQUATION; CYCLES; ONSET; LIMIT;
D O I
10.1007/s00030-022-00796-x
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The classical Kuramoto model consists of finitely many pairwisely coupled oscillators on the circle. In many applications a simple pairwise coupling is not sufficient to describe real-world phenomena as higher-order (or group) interactions take place. Hence, we replace the classical coupling law with a very general coupling function involving higher-order terms. Furthermore, we allow for multiple populations of oscillators interacting with each other through a very general law. In our analysis, we focus on the characteristic system and the mean-field limit of this generalized class of Kuramoto models. While there are several works studying particular aspects of our program, we propose a general framework to work with all three aspects (higher-order, multi-population, and mean-field) simultaneously. In this article, we investigate dynamical properties within the framework of the characteristic system. We identify invariant subspaces of synchrony patterns and study their stability. It turns out that the so called all-synchronized state, which is one special synchrony pattern, is never asymptotically stable. However, under some conditions and with a suitable definition of stability, the all-synchronized state can be proven to be at least locally stable. In summary, our work provides a rigorous mathematical framework upon which the further study of multi-population higher-order coupled particle systems can be based.
引用
收藏
页数:41
相关论文
共 50 条
  • [21] Chimera states of phase oscillator populations with nonlocal higher-order couplings
    Wu, Yonggang
    Yu, Huajian
    Zheng, Zhigang
    Xu, Can
    CHINESE PHYSICS B, 2024, 33 (04)
  • [22] Rhythmic dynamics of higher-order phase oscillator populations with competitive couplings
    Yu, Huajian
    Chen, Hongbin
    Zheng, Zhigang
    Xu, Can
    NONLINEAR DYNAMICS, 2024,
  • [23] Chimera states of phase oscillator populations with nonlocal higher-order couplings
    伍勇刚
    余华健
    郑志刚
    徐灿
    Chinese Physics B, 2024, 33 (04) : 197 - 202
  • [24] The temporal dynamics of group interactions in higher-order social networks
    Iacopini, Iacopo
    Karsai, Marton
    Barrat, Alain
    NATURE COMMUNICATIONS, 2024, 15 (01)
  • [25] Simplicial networks:a powerful tool for characterizing higher-order interactions
    Dinghua Shi
    Guanrong Chen
    National Science Review, 2022, 9 (05) : 79 - 80
  • [26] Higher-order interactions disturb community detection in complex networks
    Liu, Yuyan
    Fan, Ying
    Zeng, An
    PHYSICS LETTERS A, 2024, 494
  • [27] Predicting Biomedical Interactions With Higher-Order Graph Convolutional Networks
    Kishan, K. C.
    Li, Rui
    Cui, Feng
    Haake, Anne R.
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2022, 19 (02) : 676 - 687
  • [28] Opinion dynamics in social networks incorporating higher-order interactions
    Zhang, Zuobai
    Xu, Wanyue
    Zhang, Zhongzhi
    Chen, Guanrong
    DATA MINING AND KNOWLEDGE DISCOVERY, 2024, 38 (06) : 4001 - 4023
  • [29] Simplicial networks: a powerful tool for characterizing higher-order interactions
    Shi, Dinghua
    Chen, Guanrong
    NATIONAL SCIENCE REVIEW, 2022, 9 (05)
  • [30] Higher-Order Synaptic Interactions Coordinate Dynamics in Recurrent Networks
    Chambers, Brendan
    MacLean, Jason N.
    PLOS COMPUTATIONAL BIOLOGY, 2016, 12 (08)