Uncertainty of Resilience in Complex Networks With Nonlinear Dynamics
Uncertainty of Resilience in Complex Networks With Nonlinear Dynamics
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DOI：
10.1109/jsyst.2020.3036129
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发表时间：
2020-04
影响因子：
4.4
通讯作者：
Giannis Moutsinas;Mengbang Zou;Weisi Guo
中科院分区：
文献类型：
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作者：
Giannis Moutsinas;Mengbang Zou;Weisi Guo
弹性是系统在扰动和错误发生时保持其功能的能力。虽然我们很好地理解了低维网络系统的行为，但我们对由大量组件组成的系统的理解是有限的。近年来在预测网络水平弹性模式方面的研究促进了我们对全局网络拓扑与局部非线性组件动力学之间耦合关系的理解。然而，当模型参数存在不确定性时，对于大规模网络系统，我们对如何将其转化为弹性不确定性的理解尚不清楚。本文提出了一种多项式混沌展开方法来估计大范围不确定性分布下的弹性。通过将这种方法应用于案例研究，我们不仅揭示了拓扑和动力学子模型的一般弹性分布，而且还确定了关键方面，以便更好地监测以减少不确定性。
Resilience is a system's ability to maintain its function when perturbations and errors occur. Whilst we understand low-dimensional networked systems's behavior well, our understanding of systems consisting of a large number of components is limited. Recent research in predicting the network level resilience pattern has advanced our understanding of the coupling relationship between global network topology and local nonlinear component dynamics. However, when there is uncertainty in the model parameters, our understanding of how this translates to uncertainty in resilience is unclear for a large-scale networked system. Here we develop a polynomial chaos expansion method to estimate the resilience for a wide range of uncertainty distributions. By applying this method to case studies, we not only reveal the general resilience distribution with respect to the topology and dynamics submodels but also identify critical aspects to inform better monitoring to reduce uncertainty.