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国际多尺度计算工程期刊

每年出版 6 

ISSN 打印: 1543-1649

ISSN 在线: 1940-4352

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: 1.4 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: 1.3 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: 2.2 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: 0.00034 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: 0.46 SJR: 0.333 SNIP: 0.606 CiteScore™:: 3.1 H-Index: 31

Indexed in

A STOCHASTIC INVERSE PROBLEM FOR MULTISCALE MODELS

卷 15, 册 3, 2017, pp. 265-283
DOI: 10.1615/IntJMultCompEng.2017020553
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摘要

Descriptions of complex multiscaled physical systems often involve many physical processes interacting through a multitude of scales. In many cases, the primary interest lies in predicting behavior of the system at the macroscale (i.e., engineering scale) where continuum, physics-based, models such as partial differential equations provide high-fidelity descriptions. However, in multiscale systems, the behavior of continuum models can depend strongly on microscale properties and effects, which are often included in the macroscale model as a parameter field obtained by some upscaling process from a microscale model. Generally, a number of choices have to be made in choosing an upscaling procedure and the resulting representation of the parameter. These choices have a strong impact on both the fidelity and the computational efficiency of the model. Thus, choosing a good parameter representation and upscaling procedure becomes part of the uncertainty quantification and prediction problem for a multiscale model. We consider the use of output data from the macroscale model to formulate and solve a stochastic inverse problem to determine probability information about the upscaled parameter field. In particular, we extend a measure-theoretic inverse problem frame-work and non-intrusive sample-based algorithm to determine the choices of parameter representation and upscaling procedure that are most probable given uncertain data from the macroscale model.We illustrate the methodology in the context of shallow water flow and sub-surface flow.

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