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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

OPTIMAL UNCERTAINTY QUANTIFICATION OF A RISK MEASUREMENT FROM A THERMAL-HYDRAULIC CODE USING CANONICAL MOMENTS

Volume 10, Edição 1, 2020, pp. 35-53
DOI: 10.1615/Int.J.UncertaintyQuantification.2020030800
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RESUMO

In uncertainty quantification studies, a major topic of interest lies in assessing the uncertainties tainting the results of a computer simulation. In this work we seek to gain robustness on the quantification of a risk measurement by accounting for all sources of uncertainties tainting the inputs of a computer code. To that end, we evaluate the maximum quantile over a class of bounded distributions satisfying moments constraint. Two options are available when dealing with such complex optimization problems: one can either optimize under constraints, or preferably, one should reformulate the objective function. We identify a well suited parameterization to compute the maximal quantile based on the theory of canonical moments. It allows an effective, free of constraints, optimization. This methodology is applied to an industrial computer code related to nuclear safety.

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CITADO POR
  1. Stenger Jérôme, Gamboa Fabrice, Keller Merlin, Optimization of Quasi-convex Function over Product Measure Sets, SIAM Journal on Optimization, 31, 1, 2021. Crossref

  2. Gauchy Clement, Stenger Jerome, Sueur Roman, Iooss Bertrand, An Information Geometry Approach to Robustness Analysis for the Uncertainty Quantification of Computer Codes, Technometrics, 64, 1, 2022. Crossref

  3. Miska Niklas, Balzani Daniel, Efficient Computation of the Sharpest Bounds on the Probability of Failure of a Sheet Metal Forming Process, PAMM, 21, 1, 2021. Crossref

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  5. Sun Xingsheng, Uncertainty Quantification of Material Properties in Ballistic Impact of Magnesium Alloys, Materials, 15, 19, 2022. Crossref

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