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International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

COMPARISON OF LINEARIZATION AND GRAPH CLUSTERING METHODS FOR UNCERTAINTY QUANTIFICATION OF LARGE SCALE DYNAMICAL SYSTEMS

Volume 7, Edição 1, 2017, pp. 23-56
DOI: 10.1615/Int.J.UncertaintyQuantification.2016017192
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RESUMO

The behavior of large nonlinear dynamic systems underlying complex networked systems is hard to predict. Uncertainty quantification (UQ) in such systems by conventional methods requires high computational time, and the accuracy obtained in estimating the state variables can also be low. This paper presents a novel computational method focused on performing effective uncertainty quantification in large networked systems comprising weakly coupled subsystems (WCSs). Our approach to model complex systems is to represent them as networks (graphs) whose nodes represent the dynamical units, and whose links stand for the interactions between them. We present time-domain and space-domain linearization techniques and outline a framework that integrates the concept of linearization with graph clustering algorithm to identify WCSs in high-dimensional complex networks. The outlined technique enables identification of WCSs and thus facilitates effective UQ. The work presented in this paper also highlights the review and analytic comparison of a couple of clustering techniques [spectral clustering (SC) and non-negative matrix factorization (NMF)] that are applicable in the domain of UQ of large scale dynamical systems. The SC and NMF based clustering methods have been applied to study the UQ of scalable coupled oscillators. A new metric has been developed to compare the performance of the clustering algorithms in the UQ domain. Also, key factors that affect the performance of the algorithms have been identified. We also present the results of the statistical analysis to identify the key factors contributing to the performance of the clustering based system decomposition framework.

CITADO POR
  1. Wang Qi, Wang Yinhe, Gao Zilin, Initial State Causes the Structural Balance of Complex Networks With Dynamical Models, IEEE Access, 8, 2020. Crossref

  2. Mukherjee Arpan, Rai Rahul, Singla Puneet, Singh Tarunraj, Patra Abani, Overlapping Clustering Based Technique for Scalable Uncertainty Quantification in Physical Systems, SIAM/ASA Journal on Uncertainty Quantification, 8, 3, 2020. Crossref

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