Spectral principle for frequency synchronization in repulsive laser networks and beyond.

IF 2.7 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-02-01 DOI:10.1063/5.0251322
Mostafa Honari-Latifpour, Jiajie Ding, Igor Belykh, Mohammad-Ali Miri
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引用次数: 0

Abstract

Network synchronization of lasers is critical for achieving high-power outputs and enabling effective optical computing. However, the role of network topology in frequency synchronization of optical oscillators and lasers remains not well understood. Here, we report our significant progress toward solving this critical problem for networks of heterogeneous laser model oscillators with repulsive coupling. We discover a general approximate principle for predicting the onset of frequency synchronization from the spectral knowledge of a complex matrix representing a combination of the signless Laplacian induced by repulsive coupling and a matrix associated with intrinsic frequency detuning. We show that the gap between the two smallest eigenvalues of the complex matrix generally controls the coupling threshold for frequency synchronization. In stark contrast with attractive networks, we demonstrate that local rings and all-to-all networks prevent frequency synchronization, whereas full bipartite networks have optimal synchronization properties. Beyond laser models, we show that, with a few exceptions, the spectral principle can be applied to repulsive Kuramoto networks. Our results provide guidelines for optimal designs of scalable optical oscillator networks capable of achieving reliable frequency synchronization.

排斥激光网络及其他频率同步的频谱原理。
激光器的网络同步对于实现高功率输出和实现有效的光学计算至关重要。然而,网络拓扑在光振荡器和激光器频率同步中的作用仍然没有得到很好的理解。在这里,我们报告了我们在解决具有排斥耦合的非均质激光模型振荡器网络这一关键问题方面的重大进展。我们发现了一个一般的近似原理,用于预测频率同步的开始,从一个复杂矩阵的频谱知识表示由排斥耦合引起的无符号拉普拉斯矩阵和与本征频率失谐相关的矩阵的组合。我们证明了复矩阵的两个最小特征值之间的间隙通常控制频率同步的耦合阈值。与吸引网络形成鲜明对比的是,我们证明了局部环和全对全网络阻止频率同步,而全二部网络具有最佳同步特性。除了激光模型,我们表明,除了少数例外,光谱原理可以应用于排斥Kuramoto网络。我们的研究结果为实现可靠频率同步的可扩展光振荡器网络的优化设计提供了指导。
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
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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