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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)

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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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ORTHOGONAL POLYNOMIAL EXPANSIONS FOR SOLVING RANDOM EIGENVALUE PROBLEMS

Том 1, Выпуск 3, 2011, pp. 241-255
DOI: 10.1615/Int.J.UncertaintyQuantification.v1.i3.40
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Краткое описание

In the literature, space-filling Latin hypercube sample designs typically are generated by optimizing some criteria such as maximizing the minimum distance between points or minimizing discrepancy. However, such methods are time consuming and frequently produce designs that are highly regular, which can bias results. A fast way to generate irregular space-filling Latin hypercube sample designs is to randomly distribute the sample points to a pre-selected set of well-spaced bins. Such designs are said to be ”binning optimal” and are shown to be irregular. Specifically, Fourier analysis reveals regular patterns in the multi-dimensional spacing of points for the Sobol sequence but not for Binning optimal symmetric Latin hypercube sampling. For M = 2r ≤ 8 dimensions and N = 2s ≥ 2M points, where r and s are non-negative integers, simple patterns can be used to create a list of maximally spaced bins. Good Latin hypercube sample designs for non-power of two dimensions can be generated by discarding excess dimensions. Since the octants/bins containing the 2M end points of an ”orientation” (a rotated set of orthogonal axes) are maximally spaced, the process of generating the list of octants simplifies to finding a list of maximally spaced orientations. Even with this simplification, the ”patterns” for maximally spaced bins in M ≥ 16 dimensions are not so simple. In this paper, we use group theory to generate 2M / (2M) disjoint orientations, and present an algorithm to sort these into maximally spaced order. Conceptually, the procedure works for arbitrarily large numbers of dimensions. However, memory requirements currently preclude even listing the 2M / (2M) orientation leaders for M ≥ 32 dimensions. In anticipation of overcoming this obstacle, we outline a variant of the sorting algorithm with a low memory requirement for use in M ≥ 32 dimensions.

ЦИТИРОВАНО В
  1. Lebon Jérémy, Le Quilliec Guénhaël, Breitkopf Piotr, Filomeno Coelho Rajan, Villon Pierre, Fat Latin Hypercube Sampling and Efficient Sparse Polynomial Chaos Expansion for Uncertainty Propagation on Finite Precision Models: Application to 2D Deep Drawing Process, in Computational Methods for Solids and Fluids, 41, 2016. Crossref

  2. Li Yongsheng, Li Congbo, Garg Akhil, Gao Liang, Li Wei, Heat dissipation analysis and multi-objective optimization of a permanent magnet synchronous motor using surrogate assisted method, Case Studies in Thermal Engineering, 27, 2021. Crossref

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