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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

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MODEL REDUCTION FOR LARGE-SCALE EARTHQUAKE SIMULATION IN AN UNCERTAIN 3D MEDIUM

Volume 10, Numéro 2, 2020, pp. 101-127
DOI: 10.1615/Int.J.UncertaintyQuantification.2020031165
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RÉSUMÉ

In this paper, we are interested in the seismic wave propagation into an uncertain medium. To this end, we performed an ensemble of 400 large-scale simulations that requires 4 million core-hours of CPU time. In addition to the large computational load of these simulations, solving the uncertainty propagation problem requires dedicated procedures to handle the complexities inherent to large dataset size and the low number of samples. We focus on the peak ground motion at the free surface of the 3D domain, and our analysis utilizes a surrogate model combining two key ingredients for complexity mitigation: (i) a dimension reduction technique using empirical orthogonal basis functions, and (ii) a functional approximation of the uncertain reduced coordinates by polynomial chaos expansions. We carefully validate the resulting surrogate model by estimating its predictive error using bootstrap, truncation, and cross-validation procedures. The surrogate model allows us to compute various statistical information of the uncertain prediction, including marginal and joint probability distributions, interval probability maps, and 2D fields of global sensitivity indices.

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CITÉ PAR
  1. De Martin F, Chaljub E, Thierry P, Sochala P, Dupros F, Maufroy E, Hadri B, Benaichouche A, Hollender F, Influential parameters on 3-D synthetic ground motions in a sedimentary basin derived from global sensitivity analysis, Geophysical Journal International, 227, 3, 2021. Crossref

  2. Anquez Pierre, Glinsky Nathalie, Cupillard Paul, Caumon Guillaume, Impacts of geometric model simplifications on wave propagation—application to ground motion simulation in the lower Var valley basin (France), Geophysical Journal International, 229, 1, 2021. Crossref

  3. Sochala Pierre, Chiaberge Christophe, Claret Francis, Tournassat Christophe, Uncertainty propagation in pore water chemical composition calculation using surrogate models, Scientific Reports, 12, 1, 2022. Crossref

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