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International Journal for Uncertainty Quantification

年間 6 号発行

ISSN 印刷: 2152-5080

ISSN オンライン: 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

ITERATIVE METHODS FOR SCALABLE UNCERTAINTY QUANTIFICATION IN COMPLEX NETWORKS

巻 2, 発行 4, 2012, pp. 413-439
DOI: 10.1615/Int.J.UncertaintyQuantification.2012004138
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要約

In this paper we address the problem of uncertainty management for robust design, and verification of large dynamic networks whose performance is affected by an equally large number of uncertain parameters. Many such networks (e.g., power, thermal, and communication networks) are often composed of weakly interacting subnetworks. We propose intrusive and nonintrusive iterative schemes that exploit such weak interconnections to overcome the dimensionality curse associated with traditional uncertainty quantification methods (e.g., generalized polynomial chaos, probabilistic collocation) and accelerate uncertainty propagation in systems with a large number of uncertain parameters. This approach relies on integrating graph theoretic methods and waveform relaxation with generalized polynomial chaos, and probabilistic collocation, rendering these techniques scalable. We introduce an approximate Galerkin projection that based on the results of graph decomposition computes "strong" and "weak" influence of parameters on states. An appropriate order of expansion, in terms of the parameters, is then selected for the various states. We analyze convergence properties of this scheme and illustrate it in several examples.

によって引用された
  1. Sahai Tuhin, Pasini José Miguel, Uncertainty quantification in hybrid dynamical systems, Journal of Computational Physics, 237, 2013. Crossref

  2. Miguel Pasini José, Sahai Tuhin, Polynomial chaos based uncertainty quantification in Hamiltonian, multi-time scale, and chaotic systems, Journal of Computational Dynamics, 1, 2, 2014. Crossref

  3. Sahai Tuhin, Pasini Jose Miguel, Uncertainty quantification in hybrid dynamical systems, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012. Crossref

  4. Mukherjee Arpan, Rai Rahul, Singla Puneet, Singh Tarunraj, Patra Abani, Laplacian graph based approach for uncertainty quantification of large scale dynamical systems, 2015 American Control Conference (ACC), 2015. Crossref

  5. Streif Stefan, Kim Kwang-Ki K., Rumschinski Philipp, Kishida Masako, Shen Dongying Erin, Findeisen Rolf, Braatz Richard D., Robustness analysis, prediction, and estimation for uncertain biochemical networks: An overview, Journal of Process Control, 42, 2016. Crossref

  6. Sahai Tuhin, Dynamical Systems Theory and Algorithms for NP-hard Problems, in Advances in Dynamics, Optimization and Computation, 304, 2020. Crossref

  7. Zhu Hongyu, Klus Stefan, Sahai Tuhin, A Dynamic Mode Decomposition Approach for Decentralized Spectral Clustering of Graphs, 2022 IEEE Conference on Control Technology and Applications (CCTA), 2022. Crossref

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