Uncertainty of resilience in complex networks with nonlinear dynamics

dc.contributor.authorMoutsinas, Giannis
dc.contributor.authorZou, Mengbang
dc.contributor.authorGuo, Weisi
dc.date.accessioned2021-02-10T16:26:13Z
dc.date.available2021-02-10T16:26:13Z
dc.date.issued2020-11-24
dc.description.abstractResilience 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.en_UK
dc.identifier.citationMoutsinas G, Zou M, Guo W. (2021) Uncertainty of resilience in complex networks with nonlinear dynamics. IEEE Systems Journal, Volume 15, Number 3, September 2021, pp. 4687-4695en_UK
dc.identifier.issn1932-8184
dc.identifier.urihttps://doi.org/10.1109/JSYST.2020.3036129
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16333
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/*
dc.subjectDynamic complex networken_UK
dc.subjectresilienceen_UK
dc.subjectuncertaintyen_UK
dc.titleUncertainty of resilience in complex networks with nonlinear dynamicsen_UK
dc.typeArticleen_UK

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