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International Journal for Multiscale Computational Engineering

年間 6 号発行

ISSN 印刷: 1543-1649

ISSN オンライン: 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

MULTISCALE MODELING FOR STOCHASTIC FOREST DYNAMICS

巻 12, 発行 4, 2014, pp. 319-329
DOI: 10.1615/IntJMultCompEng.2014010276
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要約

This paper investigates a multiscale characterization of individual-based dynamics. At the fine scale, an individual-based and spatially explicit point process model is used to describe ecological processes relevant to the evolution of individuals and their mutual interactions. At the coarse scale, a stochastic model is synthesized to describe the dynamics of the system on larger spatial scales. The upscaling technique is designed to efficiently coarse-grain very fine-scale variables in order to express new state dynamics that no longer require access to fine details. The coarse dynamics are carefully constructed to preserve fluctuations from localized competition by retaining subgrid information obtained from precomputed Monte Carlo simulations of the fine-scale dynamics. Implications to computational efficiency and cross-scale coupling are significant. The present technique is then described in detail as it is applied to an individual-based and spatially explicit forest dynamics model.

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