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International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 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

CORRELATION VISUALIZATION FOR STRUCTURAL UNCERTAINTY ANALYSIS

Volume 3, Issue 2, 2013, pp. 171-186
DOI: 10.1615/Int.J.UncertaintyQuantification.2012003934
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ABSTRACT

In uncertain scalar fields, where the values at every point can he assumed as realizations of a random variable, standard deviations indicate the strength of possible variations of these values from their mean values, independently of the values at any other point in the domain. To infer the possible variations at different points relative to each other, and thus to predict the possible structural occurrences, i.e., the structural variability, of particular features in the data, the correlation between the values at these points has to be considered. The purpose of this paper is to shed light on the use of correlation as an indicator for the structural variability of isosurfaces in uncertain three-dimensional scalar fields. In a number of examples, we first demonstrate some general conclusions one can draw from the correlations in uncertain data regarding its structural variability. We will further explain, why an adequate correlation visualization is crucial for a comprehensive uncertainty analysis. Then, our focus is on the visualization of local and usually anisotropic correlation structures in the vicinity of uncertain isosurfaces. Therefore, we propose a model that can represent anisotropic correlation structures on isosurfaces and allows visual distinguishing of the local correlations between points on the surface and along the surface's normal directions. A glyph-based approach is used to simultaneously visualize these dependencies. The practical relevance of our work is demonstrated in artificial and real-world examples using standard random distributions and ensemble simulations.

CITED BY
  1. Mihai Mihaela, Westermann Rüdiger, Visualizing the stability of critical points in uncertain scalar fields, Computers & Graphics, 41, 2014. Crossref

  2. Liebmann T., Scheuermann G., Critical Points of Gaussian-Distributed Scalar Fields on Simplicial Grids, Computer Graphics Forum, 35, 3, 2016. Crossref

  3. Didandeh Arman, Sedig Kamran, Externalization of Data Analytics Models:, in Human Interface and the Management of Information: Information, Design and Interaction, 9734, 2016. Crossref

  4. Nonaka Jorji, Sakamoto Naohisa, Maejima Yasumitsu, Ono Kenji, Koyamada Koji, A visual causal exploration framework case study, SIGGRAPH Asia 2017 Symposium on Visualization, 2017. Crossref

  5. Liebmann Tom, Weber Gunther H., Scheuermann Gerik, Hierarchical Correlation Clustering in Multiple 2D Scalar Fields, Computer Graphics Forum, 37, 3, 2018. Crossref

  6. Yan Lin, Wang Yusu, Munch Elizabeth, Gasparovic Ellen, Wang Bei, A Structural Average of Labeled Merge Trees for Uncertainty Visualization, IEEE Transactions on Visualization and Computer Graphics, 26, 1, 2020. Crossref

  7. Wang Junpeng, Hazarika Subhashis, Li Cheng, Shen Han-Wei, Visualization and Visual Analysis of Ensemble Data: A Survey, IEEE Transactions on Visualization and Computer Graphics, 25, 9, 2019. Crossref

  8. Liu Can, Li Yanda, Yang Changhe, Yuan Xiaoru, Event-based exploration and comparison on time-varying ensembles, Journal of Visualization, 23, 1, 2020. Crossref

  9. Nguyen Hoa, Rosen Paul, Wang Bei, Visual exploration of multiway dependencies in multivariate data, SIGGRAPH ASIA 2016 Symposium on Visualization, 2016. Crossref

  10. Xu Haowen, Berres Andy, Thakur Gautam, Sanyal Jibonananda, Chinthavali Supriya, EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models, Journal of Biomedical Informatics, 124, 2021. Crossref

  11. Kumpf Alexander, Rautenhaus Marc, Riemer Michael, Westermann Rudiger, Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities, IEEE Transactions on Visualization and Computer Graphics, 25, 1, 2019. Crossref

  12. Evers Marina, Huesmann Karim, Linsen Lars, Uncertainty‐aware Visualization of Regional Time Series Correlation in Spatio‐temporal Ensembles, Computer Graphics Forum, 40, 3, 2021. Crossref

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