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更快的网络中断来自分层振荡动力学。

Faster network disruption from layered oscillatory dynamics.

机构信息

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA and Center for Nonlinear Studies (CNLS), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.

出版信息

Chaos. 2022 Dec;32(12):121102. doi: 10.1063/5.0129123.

Abstract

Nonlinear complex network-coupled systems typically have multiple stable equilibrium states. Following perturbations or due to ambient noise, the system is pushed away from its initial equilibrium, and, depending on the direction and the amplitude of the excursion, it might undergo a transition to another equilibrium. It was recently demonstrated [M. Tyloo, J. Phys. Complex. 3 03LT01 (2022)] that layered complex networks may exhibit amplified fluctuations. Here, I investigate how noise with system-specific correlations impacts the first escape time of nonlinearly coupled oscillators. Interestingly, I show that, not only the strong amplification of the fluctuations is a threat to the good functioning of the network but also the spatial and temporal correlations of the noise along the lowest-lying eigenmodes of the Laplacian matrix. I analyze first escape times on synthetic networks and compare noise originating from layered dynamics to uncorrelated noise.

摘要

非线性复杂网络耦合系统通常具有多个稳定的平衡点。在受到扰动或环境噪声的影响下,系统会偏离初始平衡点,并且根据偏离的方向和幅度,系统可能会发生到另一个平衡点的跃迁。最近的研究表明[M. Tyloo, J. Phys. Complex. 3 03LT01 (2022)],分层复杂网络可能会表现出放大的涨落。在这里,我研究了具有系统特定相关性的噪声如何影响非线性耦合振荡器的首次逃逸时间。有趣的是,我表明,不仅是涨落的强烈放大对网络的良好运行构成威胁,而且噪声沿拉普拉斯矩阵的最低本征模的空间和时间相关性也是如此。我在合成网络上分析了首次逃逸时间,并将源自分层动力学的噪声与不相关噪声进行了比较。

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