VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations

We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate model for parameter space exploration of ensemble simulations that allows high-resolution visualizations and user-specified visual mappings. Surrogate-enabled parameter space exploration allows domain scientists to prev...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2023-01, Vol.29 (1), p.820-830
Main Authors: Shi, Neng, Xu, Jiayi, Li, Haoyu, Guo, Hanqi, Woodring, Jonathan, Shen, Han-Wei
Format: Article
Language:English
Subjects:
Computational modeling
Data models
Data visualization
ensemble visualization
High resolution
Mathematical models
Neural networks
Parameter space exploration
Parameters
Predictive models
Representations
Simulation
Source code
Space exploration
surrogate modeling
Training
view-dependent visualization
Visualization
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Description
Summary:We propose VDL-Surrogate, a view-dependent neural-network-latent-based surrogate model for parameter space exploration of ensemble simulations that allows high-resolution visualizations and user-specified visual mappings. Surrogate-enabled parameter space exploration allows domain scientists to preview simulation results without having to run a large number of computationally costly simulations. Limited by computational resources, however, existing surrogate models may not produce previews with sufficient resolution for visualization and analysis. To improve the efficient use of computational resources and support high-resolution exploration, we perform ray casting from different viewpoints to collect samples and produce compact latent representations. This latent encoding process reduces the cost of surrogate model training while maintaining the output quality. In the model training stage, we select viewpoints to cover the whole viewing sphere and train corresponding VDL-Surrogate models for the selected viewpoints. In the model inference stage, we predict the latent representations at previously selected viewpoints and decode the latent representations to data space. For any given viewpoint, we make interpolations over decoded data at selected viewpoints and generate visualizations with user-specified visual mappings. We show the effectiveness and efficiency of VDL-Surrogate in cosmological and ocean simulations with quantitative and qualitative evaluations. Source code is publicly available at https://github.com/trainsn/VDL-Surrogate .
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2022.3209413