Explosive synchronization in interacting star networks

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2024-11-14 DOI:10.1016/j.jocs.2024.102469

Ruby Varshney , Anjuman Ara Khatun , Haider Hasan Jafri

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Abstract

We study the transition to phase synchronization in an ensemble of Stuart–Landau oscillators interacting on a star network. We observe that by introducing frequency-weighted coupling and timescale variations in the dynamics of nodes, the system exhibits a first-order explosive transition to phase synchrony. Further, we extend this study to understand the nature of synchronization in the case of two coupled star networks. If the coupled star networks are identical, we observe that with increasing inter-star coupling strength, the hysteresis width initially increases, reaches a maximum value, then decreases before saturating. If the interacting star networks are non-identical, we observe that the transition to the coherent state is preceded by the occurrence of intermittent in-phase and anti-phase synchrony for small inter-star coupling. However, for large values of coupling strengths, we observe that the intermittent state disappears and the hysteresis width changes as in coupled identical star networks. We characterize these transitions by plotting the Lyapunov exponents for the system and the master stability function.

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相互作用的星形网络中的爆炸性同步

我们研究了在星形网络上相互作用的斯图尔特-朗道振荡器集合向相位同步的过渡。我们观察到，通过在节点动力学中引入频率加权耦合和时标变化，系统呈现出向相位同步的一阶爆炸性过渡。此外，我们还扩展了这项研究，以了解两个耦合星形网络的同步性质。如果耦合星体网络完全相同，我们观察到，随着星体间耦合强度的增加，滞后宽度最初会增加，达到最大值，然后在饱和前减小。如果相互作用的星体网络是非相同的，我们观察到，在过渡到相干态之前，对于较小的星间耦合，会出现间歇性的同相和反相同步。然而，当耦合强度值较大时，我们发现间歇状态消失了，滞后宽度发生了变化，就像在耦合的相同恒星网络中一样。我们通过绘制系统的李亚普诺夫指数和主稳定函数来描述这些转变。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).