[Defense] Towards Accessible and Robust Methods for Curve-Based Visualization and Analysis
Wednesday, November 20, 2024
3:30 pm - 5:00 pm
In
Partial
Fulfillment
of
the
Requirements
for
the
Degree
of
Doctor
of
Philosophy
Nguyen
Phan
will
defend
his
proposal
听Towards
Accessible
and
Robust
Methods
for
Curve-Based
Visualization
and
Analysis
Abstract
Vector fields and their analysis are widely applied in many important aero and hydrodynamical systems. Their analysis is essential in real-world applications such as simulating wind patterns in climate science and modeling blood flow in biomedical engineering. These fields are often visualized and analyzed through curve-based representations, particularly integral curves that trace paths following the vector field鈥檚 direction. The rising complexity and scale of scientific simulations have elevated the importance of these curve-based representations, as they effectively capture both local behavior and global patterns of the underlying phenomena. However, effectively exploring, evaluating, and systematizing the analysis of these complex datasets presents significant challenges. Our work addresses these challenges through three interconnected contributions. First, we introduce the Curve Segment Neighborhood Graph (CSNG), a novel graph-based representation that captures relationships between curve segments, enabling automated feature detection through community detection algorithms while supporting interactive multi-level exploration through a force-directed layout. Second, we present a comprehensive evaluation framework for assessing neighbor search strategies in curve-based vector field analysis, introducing new metrics for characterizing neighborhood configurations and providing empirical guidance for selecting appropriate search strategies. Finally, we develop a flexible client-server framework for curve-based visualization that bridges the gap between research implementations and practical applications, supporting multiple programming languages and customizable workflows. Together, these contributions form a robust, accessible foundation for curve-based data exploration and analysis, advancing both the theoretical understanding and practical capabilities in scientific visualization.
Wednesday,
November
20,
2024
3:30
PM
-
5:00
PM
PGH 501B
Dr. Guoning Chen, proposal advisor
Faculty, students, and the general public are invited.
- Location
- Room 501B, Philip Guthrie Hoffman Hall (PGH), 3551 Cullen Blvd, Houston, TX 77204, USA