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 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 | 62-72 |
Number of pages | 11 |
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 DEAC0500OR22725.
Keywords
- Deep Learning
- Scalar Field Data
- Uncertainty Quantification
- Visualization
- Volume Visualization