Uncertainty-Informed Volume Visualization using Implicit Neural Representation

Shanu Saklani, Chitwan Goel, Shrey Bansal, Zhe Wang, Soumya Dutta, Tushar M. Athawale, David Pugmire, Christopher R. Johnson

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

Abstract

The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive generalization capabilities, understanding factors such as model prediction quality, robustness, and uncertainty is crucial. These insights can enable domain scientists to make informed decisions about their data. However, DNNs inherently lack ability to estimate prediction uncertainty, necessitating new research to construct robust uncertainty-aware visualization techniques tailored for various visualization tasks. In this work, we propose uncertainty-aware implicit neural representations to model scalar field data sets effectively and comprehensively study the efficacy and benefits of estimated uncertainty information for volume visualization tasks. We evaluate the effectiveness of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout (MC-Dropout). These techniques enable uncertainty-informed volume visualization in scalar field data sets. Our extensive exploration across multiple data sets demonstrates that uncertainty-aware models produce informative volume visualization results. Moreover, integrating prediction uncertainty enhances the trustworthiness of our DNN model, making it suitable for robustly analyzing and visualizing real-world scientific volumetric data sets.

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.
Pages62-72
Number of pages11
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 DEAC0500OR22725.

Keywords

  • Deep Learning
  • Scalar Field Data
  • Uncertainty Quantification
  • Visualization
  • Volume Visualization

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