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    Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps

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    Type
    Article
    Authors
    Bhatia, H.
    Jadhav, S.
    Bremer, P.
    Guoning Chen,
    Levine, J. A.
    Nonato, L. G.
    Pascucci, V.
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2012-09
    Permanent link to this record
    http://hdl.handle.net/10754/598343
    
    Metadata
    Show full item record
    Abstract
    Robust analysis of vector fields has been established as an important tool for deriving insights from the complex systems these fields model. Traditional analysis and visualization techniques rely primarily on computing streamlines through numerical integration. The inherent numerical errors of such approaches are usually ignored, leading to inconsistencies that cause unreliable visualizations and can ultimately prevent in-depth analysis. We propose a new representation for vector fields on surfaces that replaces numerical integration through triangles with maps from the triangle boundaries to themselves. This representation, called edge maps, permits a concise description of flow behaviors and is equivalent to computing all possible streamlines at a user defined error threshold. Independent of this error streamlines computed using edge maps are guaranteed to be consistent up to floating point precision, enabling the stable extraction of features such as the topological skeleton. Furthermore, our representation explicitly stores spatial and temporal errors which we use to produce more informative visualizations. This work describes the construction of edge maps, the error quantification, and a refinement procedure to adhere to a user defined error bound. Finally, we introduce new visualizations using the additional information provided by edge maps to indicate the uncertainty involved in computing streamlines and topological structures. © 2012 IEEE.
    Citation
    Bhatia H, Jadhav S, Bremer P, Guoning Chen, Levine JA, et al. (2012) Flow Visualization with Quantified Spatial and Temporal Errors Using Edge Maps. IEEE Transactions on Visualization and Computer Graphics 18: 1383–1396. Available: http://dx.doi.org/10.1109/TVCG.2011.265.
    Sponsors
    This work is supported in part by the National Science Foundation awards IIS-1045032, OCI-0904631, OCI-0906379, and CCF-0702817, and by King Abdullah University of Science and Technology (KAUST) Award No. KUS-C1-016-04. This work was also performed under the auspices of the US Department of Energy (DOE) by the University of Utah under contracts DE-SC0001922, DE-AC52-07NA27344, and DE-FC02-06ER25781, and Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07NA27344. The authors are grateful to Jackie Chen for the data set from Fig. 13, Robert S. Laramee for the combustion chamber data set from Fig. 17, and Paul Miller, William Cabot, and Andrew Cook for the bubbles data set from Fig. 16. We also thank Mathew Maltude from the Climate, Ocean and Sea Ice Modelling program at Los Alamos National Laboratory (LANL) and the BER Office of Science UV-CDAT team for providing us access to the ocean data from Figs. 22 and 23. Attila Gyulassy and Philippe P. Pebay provided many useful comments and discussions. LLNL-JRNL-485511.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Visualization and Computer Graphics
    DOI
    10.1109/TVCG.2011.265
    PubMed ID
    22025747
    ae974a485f413a2113503eed53cd6c53
    10.1109/TVCG.2011.265
    Scopus Count
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