Uncertainty quantification of multi-scale resilience in networked systems with nonlinear dynamics using arbitrary polynomial chaos.
Uncertainty quantification of multi-scale resilience in networked systems with nonlinear dynamics using arbitrary polynomial chaos.
复制标题
DOI：
10.1038/s41598-022-27025-w
复制
发表时间：
2023-01-10
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
作者：
复杂系统通过复杂网络连接各个组件动态来获得复杂的行为动态。复杂系统的弹性是在扰动后恢复期望行为的关键能力。在过去的几年里，我们对大规模网络弹性的理解主要局限于专有的基于代理的模拟或图的拓扑分析。然而，我们知道动力学和拓扑结构都很重要，系统模型不确定性的影响仍然没有解决，特别是在单个节点上。为了量化不确定性对整个网络分辨率（从宏观网络统计到单个节点动态）的弹性的影响，我们采用任意多项式混沌（aPC）扩展方法来确定节点失去弹性的概率以及不同的模型参数如何对单个节点的风险做出贡献。我们测试这两个通用的网络双稳态系统，也建立了生态和劳动力通勤网络动态，以证明适用性。这个框架将帮助从业者既理解宏观尺度的行为，又进行微观尺度的干预。
Complex systems derive sophisticated behavioral dynamics by connecting individual component dynamics via a complex network. The resilience of complex systems is a critical ability to regain desirable behavior after perturbations. In the past years, our understanding of large-scale networked resilience is largely confined to proprietary agent-based simulations or topological analysis of graphs. However, we know the dynamics and topology both matter and the impact of model uncertainty of the system remains unsolved, especially on individual nodes. In order to quantify the effect of uncertainty on resilience across the network resolutions (from macro-scale network statistics to individual node dynamics), we employ an arbitrary polynomial chaos (aPC) expansion method to identify the probability of a node in losing its resilience and how the different model parameters contribute to this risk on a single node. We test this using both a generic networked bi-stable system and also established ecological and work force commuter network dynamics to demonstrate applicability. This framework will aid practitioners to both understand macro-scale behavior and make micro-scale interventions.
登录
查看更多内容
影响因子：
4.3
作者：
Lundqvist M;Compte A;Lansner A
通讯作者：
Lansner A
影响因子：
8.1
作者：
Oladyshkin, S.;Nowak, W.
通讯作者：
Nowak, W.
DOI：
10.1002/nme.6077
发表时间：
2019-08-31
影响因子：
2.9
作者：
Prince, Zachary M.;Ragusa, Jean C.
通讯作者：
Ragusa, Jean C.
影响因子：
4.3
作者：
Ron, Jonathan E.;Pinkoviezky, Itai;Gov, Nir S.
通讯作者：
Gov, Nir S.
影响因子：
64.8
作者：
Gao, Jianxi;Barzel, Baruch;Barabasi, Albert-Laszlo
通讯作者：
Barabasi, Albert-Laszlo