Balancing Accuracy and Efficiency: Compact Representations for Flow and Multivariate Visualization Amani Ageeli, Ph.D. Student, Computer Science Nov 25, 10:00 - 12:00 B3 L5 R5220 Scientific Visualization real-time rendering computer graphics interactive visualization large datasets multivariate functional data This thesis addresses the challenge of interactively visualizing massive scientific datasets by introducing novel frameworks that strategically balance accuracy and efficiency for scalable multivariate filtering, objective time-dependent flow analysis, and hybrid, complexity-guided flow reconstruction.
Observer-Relative Flow Visualization and Objective Feature Extraction Xingdi Zhang, Ph.D. Student, Computer Science Nov 19, 16:30 - 18:00 B1 L4 R4214 visual computing scientific computing deep learning This dissertation develops an integrated toolkit of novel visualization and feature extraction methods, grounded in a Riemannian geometry framework, to enable an objective, observer-relative, and physically consistent analysis of complex unsteady flows.
Interactive High-Quality Visualization of Large-Scale Particle Data Mohamed Ibrahim, Postdoctoral Research Fellow, Computer Science Nov 5, 14:00 - 15:00 B2 L5 R5209 visualization high-quality rendering large-scale simulation particle data aliasing artifact sampling visible particles Large-scale particle data sets, such as those computed in molecular dynamics (MD) simulations, are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry.
