IEEE VIS 2024 Content: An Entropy-Based Test and Development Framework for Uncertainty Modeling in Level-Set Visualizations

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

Robert Sisneros - University of Illinois Urbana-Champaign, Urbana, United States

Tushar M. Athawale - Oak Ridge National Laboratory, Oak Ridge, United States

Kenneth Moreland - Oak Ridge National Laboratory, Oak Ridge, United States

David Pugmire - Oak Ridge National Laboratory, Oak Ridge, United States

Screen-reader Accessible PDF

Room: Bayshore VI

2024-10-14T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-14T12:30:00Z
Exemplar figure, described by caption below
Representative test/result from our framework (wind dataset ensemble created via random uniform noise). The entropy for the full distribution model matches closely to the uniform distribution assumption (red boxes) and the minimum entropy with the Gaussian assumption may not always be the best representative.
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.