An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations

Robert Sisneros, Tushar Athawale, David Pugmire, Kenneth Moreland

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationProceedings - 2024 IEEE Workshop on Uncertainty Visualization
Subtitle of host publicationApplications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages78-83
Number of pages6
ISBN (Electronic)9798331527600
DOIs
StatePublished - 2024
Event2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024 - St. Pete Beach, United States
Duration: Oct 14 2024 → …

Publication series

NameProceedings - 2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024

Conference

Conference2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks, UncertaintyVis 2024
Country/TerritoryUnited States
CitySt. Pete Beach
Period10/14/24 → …

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

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