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International Journal for Uncertainty Quantification

Erscheint 6 Ausgaben pro Jahr

ISSN Druckformat: 2152-5080

ISSN Online: 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

POLYNOMIAL CHAOS FOR SEMIEXPLICIT DIFFERENTIAL ALGEBRAIC EQUATIONS OF INDEX 1

Volumen 3, Ausgabe 1, 2013, pp. 1-23
DOI: 10.1615/Int.J.UncertaintyQuantification.2011003306
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ABSTRAKT

Mathematical modeling of technical applications often yields systems of differential algebraic equations. Uncertainties of physical parameters can be considered by the introduction of random variables. A corresponding uncertainty quantification requires one to solve the stochastic model. We focus on semiexplicit systems of nonlinear differential algebraic equations with index 1. The stochastic model is solved using the expansion of the generalised polynomial chaos. We investigate both the stochastic collocation technique and the stochastic Galerkin method to determine the unknown coefficient functions. In particular, we analyze the index of the larger coupled systems, which result from the stochastic Galerkin method. Numerical simulations of test examples are presented, where the two approaches are compared with respect to their efficiency.

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