APA
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Alabi, O. S., Wu, X., Harter, J., Phadke, M., Pinto, L., Petersen, H., … Taylor, R. M. (2012). Comparative visualization of ensembles using ensemble surface slicing. In Proceedings Visualization and Data Analysis (VDA '12) (Vol. paper 0U, 8294, pp. 1–12).
Chicago/Turabian
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Alabi, O. S., X. Wu, J. Harter, M. Phadke, L. Pinto, Hannah Petersen, S. Bass, et al. “Comparative Visualization of Ensembles Using Ensemble Surface Slicing.” In Proceedings Visualization and Data Analysis (VDA '12), paper 0U, 8294:1–12, 2012.
MLA
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Alabi, O. S., et al. “Comparative Visualization of Ensembles Using Ensemble Surface Slicing.” Proceedings Visualization and Data Analysis (VDA '12), vol. paper 0U, 8294, 2012, pp. 1–12.
BibTeX Click to copy
@conference{o2012a,
title = {Comparative visualization of ensembles using ensemble surface slicing},
year = {2012},
pages = {1-12},
volume = {paper 0U, 8294},
author = {Alabi, O. S. and Wu, X. and Harter, J. and Phadke, M. and Pinto, L. and Petersen, Hannah and Bass, S. and Keifer, M. and Zhong, S. and Healey, C. G. and Taylor, R. M.},
booktitle = {Proceedings Visualization and Data Analysis (VDA '12)}
}
By definition, an ensemble is a set of surfaces or volumes derived from a series of simulations or experiments. Sometimes the series is run with different initial conditions for one parameter to determine parameter sensitivity. The understanding and identification of visual similarities and differences among the shapes of members of an ensemble is an acute and growing challenge for researchers across the physical sciences. More specifically, the task of gaining spatial understanding and identifying similarities and differences between multiple complex geometric data sets simultaneously has proved challenging. This paper proposes a comparison and visualization technique to support the visual study of parameter sensitivity. We present a novel single-image view and sampling technique which we call Ensemble Surface Slicing (ESS). ESS produces a single image that is useful for determining differences and similarities between surfaces simultaneously from several data sets. We demonstrate the usefulness of ESS on two real-world data sets from our collaborators.