Understanding and visualizing data with spatially and temporally varying frames of reference pose significant challenges across various scientific disciplines. This seminar presents a novel paradigm that simplifies the visualization and analysis of complex scientific data by representing deforming spacetime with a higher-dimensional mesh.
We illustrate the core application of our framework within the context of tokamak fusion plasma, where the interpolation of science variables, such as density and temperature, occurs within a sophisticated magnetic field-line-following coordinate system. Similarly, we shed light on analogous challenges encountered in rotational fluid mechanics, cosmology, and Lagrangian ocean analysis, wherein spacetime dynamics contribute to the intricate complexities involved in tasks such as volume rendering, isosurfacing, and feature tracking.
Central to our approach is a method that partitions 3D, deforming spacetime into smaller, independent components, facilitating a streamlined and efficient triangulation process. We introduce a decision-tree-based algorithm that enables the search for feasible triangulations, accounting for intricate geometry and connectivity constraints. This algorithm represents a key innovation, allowing for the accurate and comprehensive representation of deforming spacetime, even in the presence of nonconvex structures.
Hanqi Guo is an Associate Professor at the Department of Computer Science and Engineering at The Ohio State University. He also holds a joint appointment at the Mathematics and Computer Science Division at Argonne National Laboratory. His research areas include data analysis, visualization, and machine learning for scientific data. He is an awardee of the U.S. DOE Early Career Research Program (ECRP) in 2022 and received multiple best paper awards in premiere visualization conferences.
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