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International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 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

PROPAGATION OF UNCERTAINTY BY SAMPLING ON CONFIDENCE BOUNDARIES

Volume 3, Issue 5, 2013, pp. 421-444
DOI: 10.1615/Int.J.UncertaintyQuantification.2012004275
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ABSTRACT

A new class of methods for propagation of uncertainty through complex models nonlinear-in-parameters is proposed. It is derived from a recent idea of propagating covariance within the unscented Kalman filter. The nonlinearity could be due to a pole-zero parametrization of a dynamic model in the Laplace domain, finite element model (FEM) or other large computer models, models of mechanical fatigue etc. Two approximate methods of this class are evaluated against Monte Carlo simulations and compared to the application of the Gauss approximation formula. Three elementary static models illustrate pros and cons of the methods, while one dynamic model provides a realistic simple example of its use.

CITED BY
  1. Hessling Jan Peter, Deterministic Sampling for Propagating Model Covariance, SIAM/ASA Journal on Uncertainty Quantification, 1, 1, 2013. Crossref

  2. Hessling Jan Peter, Identification of Complex Models, SIAM/ASA Journal on Uncertainty Quantification, 2, 1, 2014. Crossref

  3. Hessling Jan Peter, Subjectivity in Application of the Principle of Maximum Entropy, Open Journal of Statistics, 03, 06, 2013. Crossref

  4. Garcia Cervantes Elias Y., Erasmus Bernard, van der Marck Steven, Fedon Christian, Quantification of uncertainties due to manufacturing tolerances using deterministic sampling methods, Nuclear Engineering and Design, 382, 2021. Crossref

  5. Fedon Christian, Cervantes Elias Y. Garcia, Salamon Lino, Erasmus Bernard, Application of deterministic sampling methods for uncertainty quantification in manufacturing tolerances in neutron physics, Nuclear Engineering and Design, 373, 2021. Crossref

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