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International Journal for Uncertainty Quantification

Publication de 6  numéros par an

ISSN Imprimer: 2152-5080

ISSN En ligne: 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

FAST AND ACCURATE MODEL REDUCTION FOR SPECTRAL METHODS IN UNCERTAINTY QUANTIFICATION

Volume 6, Numéro 3, 2016, pp. 271-286
DOI: 10.1615/Int.J.UncertaintyQuantification.2016016646
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RÉSUMÉ

A fast and accurate model order reduction procedure is presented that can successfully be applied to spectral methods for uncertainty quantification problems. The main novelties include (1) the application of model order reduction to uncertainty quantification problems; (2) the improvement of existing model order reduction methods in order to meet the accuracy and performance requirements; and (3) an efficient approach for systems with many outputs. Numerical experiments for large-scale realistic systems illustrate the suitability and performance (50× speedup while preserving accuracy) for uncertainty quantification problems.

CITÉ PAR
  1. Pulch Roland, Stability-preserving model order reduction for linear stochastic Galerkin systems, Journal of Mathematics in Industry, 9, 1, 2019. Crossref

  2. Pulch Roland, Frequency domain integrals for stability preservation in Galerkin-type projection-based model order reduction, International Journal of Control, 94, 7, 2021. Crossref

  3. Ullmann Sebastian, Müller Christopher, Lang Jens, Stochastic Galerkin Reduced Basis Methods for Parametrized Linear Convection–Diffusion–Reaction Equations, Fluids, 6, 8, 2021. Crossref

  4. Kollepara Kiran Sagar, Navarro‐Jiménez José M., Le Guennec Yves, Silva Luisa, Aguado José V., On the limitations of low‐rank approximations in contact mechanics problems, International Journal for Numerical Methods in Engineering, 2022. Crossref

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