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International Journal for Uncertainty Quantification

Publicado 6 números por año

ISSN Imprimir: 2152-5080

ISSN En Línea: 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

STOCHASTIC SPECTRAL EMBEDDING

Volumen 11, Edición 2, 2021, pp. 25-47
DOI: 10.1615/Int.J.UncertaintyQuantification.2020034395
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SINOPSIS

Constructing approximations that can accurately mimic the behavior of complex models at reduced computational costs is an important aspect of uncertainty quantification. Despite their flexibility and efficiency, classical surrogate models such as kriging or polynomial chaos expansions tend to struggle with highly nonlinear, localized, or nonstationary computational models. We hereby propose a novel sequential adaptive surrogate modeling method based on recursively embedding locally spectral expansions. It is achieved by means of disjoint recursive partitioning of the input domain, which consists in sequentially splitting the latter into smaller subdomains, and constructing simpler local spectral expansions in each, exploiting the trade-off complexity vs. locality. The resulting expansion, which we refer to as "stochastic spectral embedding" (SSE), is a piecewise continuous approximation of the model response that shows promising approximation capabilities, and good scaling with both the problem dimension and the size of the training set. We finally show how the method compares favorably against state-of-the-art sparse polynomial chaos expansions on a set of models with different complexity and input dimension.

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CITADO POR
  1. Wagner Paul-Remo, Marelli Stefano, Sudret Bruno, Bayesian model inversion using stochastic spectral embedding, Journal of Computational Physics, 436, 2021. Crossref

  2. Teixeira Rui, Martinez-Pastor Beatriz, Nogal Maria, O’Connor Alan, Metamodel-based metaheuristics in optimal responsive adaptation and recovery of traffic networks, Sustainable and Resilient Infrastructure, 2022. Crossref

  3. El Garroussi Siham, Ricci Sophie, De Lozzo Matthias, Goutal Nicole, Lucor Didier, Tackling random fields non-linearities with unsupervised clustering of polynomial chaos expansion in latent space: application to global sensitivity analysis of river flooding, Stochastic Environmental Research and Risk Assessment, 36, 3, 2022. Crossref

  4. Lüthen Nora, Marelli Stefano, Sudret Bruno, Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark, SIAM/ASA Journal on Uncertainty Quantification, 9, 2, 2021. Crossref

  5. Wagner P.-R., Marelli S., Papaioannou I., Straub D., Sudret B., Rare event estimation using stochastic spectral embedding, Structural Safety, 96, 2022. Crossref

  6. Rossat D., Baroth J., Briffaut M., Dufour F., Bayesian inversion using adaptive Polynomial Chaos Kriging within Subset Simulation, Journal of Computational Physics, 455, 2022. Crossref

  7. Meles Giovanni Angelo, Linde Niklas, Marelli Stefano, Bayesian tomography with prior-knowledge-based parametrization and surrogate modelling, Geophysical Journal International, 231, 1, 2022. Crossref

  8. Lee Ikjin, Lee Ungki, Ramu Palaniappan, Yadav Deepanshu, Bayrak Gamze, Acar Erdem, Small failure probability: principles, progress and perspectives, Structural and Multidisciplinary Optimization, 65, 11, 2022. Crossref

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