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In the scientific community, the simulation of phenomena with a broad range of potential outcomes is a common practice. These simulations are designed to determine the parameters that generate results that are consistent with empirical observations. Running many simulations is expensive, however, because both computational time and storage for the output can be prohibitively large. Recent advancements in deep learning methods offer a new and innovative approach to parameter space exploration in scientific applications. Through the application of deep learning techniques, the exploration of parameter space can be framed as either a generative or regression problem. Our research group, the GRAVITY lab, is actively investigating two distinct categories of deep learning models for this purpose: image-based and data-based surrogate models. Image-based surrogates directly predict 2D visualization images, while data-based surrogates synthesize 3D visual data, such as volumetric data. Image-based surrogates are often trained with predefined visual parameters, such as view angles and visual mappings, and typically require relatively low training costs. Data-based surrogates offer greater flexibility in terms of 3D interactions and post-processing operations, such as isosurfacing and feature extraction.
Publications:
Publications:
Neng Shi, Jiayi Xu, Haoyu Li, Hanqi Guo, Jonathan Woodring, and Han-Wei Shen: VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations, IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS 2022), 229(1), 820-830, 2023. [Best Paper Honorable Mention Award at IEEE VIS 2022]
Neng Shi, Jiayi Xu, Skylar W. Wurster, Hanqi Guo, Jonathan Woodring, Luke Van Roekel, and Han-Wei Shen: GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations, IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE PacificVis 2022), 28(6):2301-2313, 2022.
Wenbin He, Junpeng Wang, Hanqi Guo, Ko-Chih Wang, Han-Wei Shen, Mukund Raj, Youssef SG Nashed, and Tom Peterka: InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations, IEEE Transactions on Visualization and Computer Graphics 26 (1), 23-33 (2020), [Best Paper Award at IEEE VIS 2019]
Subhashis Hazarika, Haoyu Li, Ko-Chih Wang, Han-Wei Shen, and Ching-Shan Chou: NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation, IEEE Transactions on Visualization and Computer Graphics 26 (1), 34-44 (2020), [Best Paper Honorable Mention Award at IEEE VIS 2019]
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