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International Journal for Uncertainty Quantification
Главный редактор: Habib N. Najm (open in a new tab)
Ассоциированный редакторs: Dongbin Xiu (open in a new tab) Tao Zhou (open in a new tab)
Редактор-основатель: Nicholas Zabaras (open in a new tab)

Выходит 6 номеров в год

ISSN Печать: 2152-5080

ISSN Онлайн: 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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VPS: VORONOI PIECEWISE SURROGATE MODELS FOR HIGH-DIMENSIONAL DATA FITTING

Том 7, Выпуск 1, 2017, pp. 1-21
DOI: 10.1615/Int.J.UncertaintyQuantification.2016018697
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Краткое описание

Surrogate models (metamodels) are indispensable for numerical simulations over high-dimensional spaces. They typically use well-selected samples of the expensive code runs to produce a cheap-to-evaluate model. We introduce a new method to construct credible global surrogates with local accuracy without dictating where to sample: Voronoi piecewise surrogate (VPS) models. The key component in our method is to implicitly decompose the parameter space into cells using the Voronoi tessellation around the sample points as seeds, via an approximate dual Delaunay graph. While explicit domain decompositions have storage and processing requirements that exponentially grow with dimension, VPS construction counts on the implicitness of Voronoi cells and the one-to-one mapping between seeds and cells, regardless of dimension, to avoid this curse of dimensionality. Each implicit cell can then use information provided by its neighbors to build its own local piece of the global surrogate. The piecewise locality breaks down the high-order approximation problem into a set of low-order problems, with better immunity against numerical oscillations. Domain points can be assigned to cells using a simple nearest seed search. Furthermore, a VPS model is naturally updated with the addition of new samples, can handle smooth and discontinuous functions, and can adopt a parallel implementation. We demonstrate the application of VPS models to numerical integration and probability of failure estimation problems.

ЦИТИРОВАНО В
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