High Performance Structure Extraction for Visualization
The amount of scientific data produced through simulations and experiments in engineering and medicine is continuously growing. These datasets often comprise multiple variables (or fields) that describe the physics of the considered phenomena. The purpose of visualization is to provide the user with graphical tools to discover and analyze the remarkable features exhibited by these datasets. In my thesis, I approach these features from the point of view of their characterization as geometric structures. These structures often take the form of manifolds in the spati al domain. My work is premised on the idea that characterizing and visualizing these manifolds can enable or significantly improve the interpretation and analysis of spatial scientific data across problem domains.
Defining and extracting these structures is a challenging task, however. This is mainly due to the ambiguous nature of the features of interest and the computational cost of their extraction from very large-scale spatio-temporal domains. In addition, most visualization scenarios require interactivity to allow the user to steer the visual analysis, and provide a seamless exploration of the domain. In my work, I aim to devise new scalable and high-performance methods for the identification, extraction, and visualization of salient structures from 3D datasets. Applications of this research span fluid dynamics, medical imaging, and combustion research.
This talk will include a discussion of the interactive visualization of Lagrangian Coherent structures (LCS) using a massively parallel computation of the Finite-Time Lyapunov Exponent. Since LCS structures correspond to ridges of FTLE, the discussion will extend to describing parallel methods for the interactive visualization and extraction of crease manifolds from scientific data in general. The talk will also include the topic of multifiled feature definition and visualization. Finally, I will cover some new scalable ideas concerning the adaptive refinement and approximation of the flow map.