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国际不确定性的量化期刊

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ISSN 打印: 2152-5080

ISSN 在线: 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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REPLICATION-BASED EMULATION OF THE RESPONSE DISTRIBUTION OF STOCHASTIC SIMULATORS USING GENERALIZED LAMBDA DISTRIBUTIONS

卷 10, 册 3, 2020, pp. 249-275
DOI: 10.1615/Int.J.UncertaintyQuantification.2020033029
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摘要

Due to limited computational power, performing uncertainty quantification analyses with complex computational models can be a challenging task. This is exacerbated in the context of stochastic simulators, the response of which to a given set of input parameters, rather than being a deterministic value, is a random variable with unknown probability density function (PDF). Of interest in this paper is the construction of a surrogate that can accurately predict this response PDF for any input parameters. We suggest using a flexible distribution family−the generalized lambda distribution−to approximate the response PDF. The associated distribution parameters are cast as functions of input parameters and represented by sparse polynomial chaos expansions. To build such a surrogate model, we propose an approach based on a local inference of the response PDF at each point of the experimental design based on replicated model evaluations. Two versions of this framework are proposed and compared on analytical examples and case studies.

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对本文的引用
  1. Tsokanas Nikolaos, Zhu Xujia, Abbiati Giuseppe, Marelli Stefano, Sudret Bruno, Stojadinović Božidar, A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures, Frontiers in Built Environment, 7, 2021. Crossref

  2. Zhu Xujia, Sudret Bruno, Emulation of Stochastic Simulators Using Generalized Lambda Models, SIAM/ASA Journal on Uncertainty Quantification, 9, 4, 2021. Crossref

  3. Singh D, Dwight R P, Laugesen K, Beaudet L, Viré A, Probabilistic surrogate modeling of offshore wind-turbine loads with chained Gaussian processes, Journal of Physics: Conference Series, 2265, 3, 2022. Crossref

  4. Abbiati Giuseppe, Marelli Stefano, Ligeikis Connor, Christenson Richard, Stojadinović Božidar, Training of a Classifier for Structural Component Failure Based on Hybrid Simulation and Kriging, Journal of Engineering Mechanics, 148, 1, 2022. Crossref

  5. Zhu Xujia, Sudret Bruno, Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models, Reliability Engineering & System Safety, 214, 2021. Crossref

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

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