Abstract
We present a simple comparative framework for testing and developing uncertainty modeling in uncertain marching cubes implementations. The selection of a model to represent the probability distribution of uncertain values directly influences the memory use, run time, and accuracy of an uncertainty visualization algorithm. We use an entropy calculation directly on ensemble data to establish an expected result and then compare the entropy from various probability models, including uniform, Gaussian, histogram, and quantile models. Our results verify that models matching the distribution of the ensemble indeed match the entropy. We further show that fewer bins in nonparametric histogram models are more effective whereas large numbers of bins in quantile models approach data accuracy.
Original language | English |
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Title of host publication | Proceedings - 2024 IEEE Workshop on Uncertainty Visualization |
Subtitle of host publication | Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 78-83 |
Number of pages | 6 |
ISBN (Electronic) | 9798331527600 |
DOIs | |
State | Published - 2024 |
Event | 2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024 - St. Pete Beach, United States Duration: Oct 14 2024 → … |
Publication series
Name | Proceedings - 2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024 |
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Conference
Conference | 2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024 |
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Country/Territory | United States |
City | St. Pete Beach |
Period | 10/14/24 → … |
Funding
This work was supported in part by the U.S. Department of Energy (DOE) RAPIDS-2 SciDAC project under contract number DE-AC0500OR22725.
Keywords
- 300 [Human-centered computing]: Visualization application domains -
- Scientific Visualization