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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

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SENSITIVITY ANALYSIS WITH CORRELATED INPUTS: COMPARISON OF INDICES FOR THE LINEAR CASE

巻 13, 発行 6, 2023, pp. 25-56
DOI: 10.1615/Int.J.UncertaintyQuantification.2023042817
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要約

The objective of a global sensitivity analysis is to provide indices to rank the importance of each and every system input when considering the impact on a given system output. This paper discusses a few of the methods proposed throughout the literature when dealing with a linear model for which part of or all the input variables cannot be considered independently. The aim here is to review methods from the late 1980s in order to compare them to more recent developments, by investigating their underlying hypothesis, cost (in term of resource usage), and results. This paper focuses on the case where there is no assumption on the knowledge of the probability density functions, assuming that the analysis can be done from a provided sample, without the use of refined techniques which would require a dedicated surrogate model generation. After an introduction of the general problem, as often discussed in the independent approach, a review of solutions not solely relying on the variance decomposition is presented, along with their underlying hypothesis. A protocol is proposed, based on a statistical approach relying on random correlation matrix generation, to test and compare all methods with an increasingly complex, step-by-step procedure. Finally, dependencies with respect to parameters defining the problem, such as the input space size, the sample size, and the nature of the input laws, are tested before drawing conclusions on the methods and their usefulness.

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