Visualization Surrogates for Ensemble Simulations

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. 

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