Award Abstract # 1553329
CAREER: Generating Hierarchical Vector-Valued Data Summaries for Scalable Flow Data Processing, Analysis and Visualization

NSF Org: IIS
Division of Information & Intelligent Systems
Recipient: UNIVERSITY OF HOUSTON SYSTEM
Initial Amendment Date: January 8, 2016
Latest Amendment Date: March 20, 2020
Award Number: 1553329
Award Instrument: Continuing Grant
Program Manager: Hector Munoz-Avila
IIS
 Division of Information & Intelligent Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: February 1, 2016
End Date: January 31, 2023 (Estimated)
Total Intended Award Amount: $499,054.00
Total Awarded Amount to Date: $553,854.00
Funds Obligated to Date: FY 2016 = $123,301.00
FY 2017 = $121,617.00

FY 2018 = $109,903.00

FY 2019 = $124,629.00

FY 2020 = $74,404.00
History of Investigator:
  • Guoning Chen (Principal Investigator)
    gchen16@uh.edu
Recipient Sponsored Research Office: University of Houston
4300 MARTIN LUTHER KING BLVD
HOUSTON
TX  US  77204-3067
(713)743-5773
Sponsor Congressional District: 18
Primary Place of Performance: University of Houston
TX  US  77004-2015
Primary Place of Performance
Congressional District:
18
Unique Entity Identifier (UEI): QKWEF8XLMTT3
Parent UEI:
NSF Program(s): Info Integration & Informatics
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
01001718DB NSF RESEARCH & RELATED ACTIVIT

01001819DB NSF RESEARCH & RELATED ACTIVIT

01001920DB NSF RESEARCH & RELATED ACTIVIT

01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 1045, 7364, 9251
Program Element Code(s): 736400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Vector fields are a ubiquitous tool to describe the behaviors of various dynamical systems that dominate many important physical phenomena, especially fluids. Their analysis is indispensable for many applications ranging from medical data analysis such as blood circulation to tsunami simulations and many other problems in science and engineering. However, processing and interpreting very large scale vector field data defined in a high dimensional space has become the bottleneck of many critical scientific research tasks. More specifically, the analysis of vector field data, that is inherently complex and formidable in size, is particularly challenging especially when visualization is limited by the finite resolution and dimensions of modern displays, and the amount of information conveyed via the visualization is constrained by the limited bandwidth of the human visual perception channel. This problem cannot be solved without a comprehensive, summary representation of vector field flow data, which has not been well studied in the flow visualization community. The proposed research will fill this gap. Theoretical contributions of this research will impact methods in computational topology, fluid mechanics, and mathematics, while its applications will benefit a wide variety of disciplines including climate study, physics, chemistry, mechanical and civil engineering, and cardiovascular disease diagnosis. The results of this work will be incorporated into new courses in the area of vector field data processing and visualization at both the undergraduate and graduate levels that will benefit students of a broad range of disciplines.

A vector field is a function that assigns any spatial point a vector value describing the displacement of objects. To develop an effective summary representation for vector fields, the proposed project will first study the relations between different flow characteristics and descriptors, aiming to reduce the redundancy in the extraction of the summary. Second, a novel link-graph hybrid representation will be developed with the goal of seamlessly integrating various flow information, characterized from different perspectives and in various scales, into a dimension-independent representation. Third, based on this intermediate representation, a new and scalable vector field analysis framework will be developed, from which a hierarchical summary for vector field data can be defined. The information theoretical framework will be adapted to evaluate the information loss in the summary representation. This summary will enable a number of applications for scientific discovery and education including the scalable and knowledge-assisted exploration of flow data, vector field comparison, and vector field synthesis gaming. The knowledge obtained during this project will be adapted to study the summary representation of more complex data, such as tensor field data. More importantly, this research represents one step towards a unified framework of knowledge discovery and integrity from heterogeneous data sources. The developed theory and algorithms will be published in peer-reviewed journal and conferences. The project webpage (http://www2.cs.uh.edu/~chengu/Hier_VVDSummary/Hier_VVDSummary.html) will provide brief description of the key outcome and links or pointers to the corresponding publications and generated datasets. The developed software, libraries, plug-ins, and open source code will be released on the on the project webpage and Github.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 35)
Amirhossein Arzani, Alberto M. Gambaruto, Guoning Chen, and Shawn C. Shadden "Wall shear stress exposure time: A Lagrangian measure of near-wall stagnation and concentration in cardiovascular flows" Biomechanics and Modeling in Mechanobiology , 2017
Berenjkoub, Marzieh and Monico, Rodolfo Ostilla and Laramee, Robert S. and Chen, Guoning "Visual Analysis of Spatia-temporal Relations of Pairwise Attributes in Unsteady Flow" IEEE Transactions on Visualization and Computer Graphics , v.25 , 2019 10.1109/TVCG.2018.2864817 Citation Details
Duong B. Nguyen, Lei Zhang, Rodolfo Ostilla Monico, David Thompson, Robert S. Laramee, and Guoning Chen "Physics based Pathline Clustering and Exploration" Computer Graphics Forum , v.40 , 2021 , p.22-37 10.1111/cgf.14093
Duong B. Nguyen, Lei Zhang, Rodolfo Ostilla Monico, David Thompson, Robert S. Laramee, and Guoning Chen "Unsteady Flow Visualization via Physics based Pathline Exploration" IEEE Visualization 2019 Short Papers , 2019
Duong B. Nguyen, Rodolfo Ostilla Monico, and Guoning Chen "A Visualization Framework for Multi-scale Coherent Structures in Taylor-Couette Turbulence" IEEE Transactions on Visualization and Computer Graphics , v.27 , 2021 , p.902-912 10.1109/TVCG.2020.3028892
Gao, Xifeng and Huang, Jin and Xu, Kaoji and Pan, Zherong and Deng, Zhigang and Chen, Guoning "Evaluating Hex-mesh Quality Metrics via Correlation Analysis" Computer Graphics Forum , v.36 , 2017 10.1111/cgf.13249 Citation Details
Gao, Xifeng and Panozzo, Daniele and Wang, Wenping and Deng, Zhigang and Chen, Guoning "Robust structure simplification for hex re-meshing" ACM Transactions on Graphics , v.36 , 2017 10.1145/3130800.3130848 Citation Details
Govyadinov, Pavel A. and Womack, Tasha and Eriksen, Jason L. and Chen, Guoning and Mayerich, David "Robust Tracing and Visualization of Heterogeneous Microvascular Networks" IEEE Transactions on Visualization and Computer Graphics , v.25 , 2019 10.1109/TVCG.2018.2818701 Citation Details
Govyadinov, Pavel and Womack, Tasha and Eriksen, Jason and Mayerich, David and Chen, Guoning "Graph-Assisted Visualization of Microvascular Networks" 2019 IEEE Visualization Conference (VIS) Short Papers , 2019 10.1109/VISUAL.2019.8933682 Citation Details
Kaoji Xu and Guoning Chen "Hexahedral Mesh Structure Visualization and Evaluation" IEEE Transactions on Visualization and Computer Graphics (IEEE SciVis 2018) , v.25 , 2019 , p.1173
Kaoji Xu, Muhammad Naeem Akram, and Guoning Chen "Semi-global Quad Mesh Structure Simplification via Separatrix Operations" ACM SIGGRAPH ASIA 2020 Technical Communications , 2020 10.1145/3410700.3425436
(Showing: 1 - 10 of 35)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

     Vector fields are a fundamental tool used to describe the behaviors of various dynamical systems that play a crucial role in many important physical phenomena, particularly fluids. Analyzing vector fields is essential for numerous applications, ranging from medical data analysis, such as blood circulation, to modeling natural disasters like tsunamis, and many other scientific and engineering problems. However, most existing analysis and visualization techniques for vector fields are insufficiently scalable to handle increasingly large and detailed real-world data. This issue is compounded by the finite resolution and dimensions of modern displays and the limited capacity of human visual perception channels, which make it challenging to comprehend this inherently complex data in its entirety or at a fine level of detail.

     This project seeks to tackle the aforementioned challenges by developing an innovative summary representation of vector fields that can enable efficient storage, processing, and interpretation of flow data from diverse engineering, scientific, and medical applications. The project aims to engage both graduate and undergraduate students in this research to cultivate the next generation of STEM researchers. Moreover, the project plans to integrate the research outcomes into the curriculum development for the Department of Computer Science at the University of Houston, thereby providing students with valuable exposure to cutting-edge research in this field.

     This project has resulted in several key outcomes. Firstly, a physics-based vector field analysis and exploration framework has been proposed. This framework encodes the physical attributes of flow particles to their respective trajectories and conducts physics-aware clustering of these trajectories. This enables the users to easily identify important features and structures in the flow. Secondly, a summary and reduced representation of trajectories of flow particles has been introduced to reduce the dense representation of 3D flow into a sparse but informative representation. This facilitates the depiction and exploration of the essential behaviors of complex 3D flow. Thirdly, a first study on the co-variant behaviors of different geometric and physical flow attributes over time has been conducted, which has led to an improved understanding of the flow behaviors and facilitates the exploration of the causal relations among flow characteristics. Fourthly, a few techniques for the extraction of multi-scale coherent structures and their visualizations for turbulent flows have been proposed. These techniques and frameworks enrich the toolkits of fluid mechanists for their study of some of the most complex flows. Fifthly, a suite of innovative algorithms and pipelines for the generation and optimization of 2D quadrilateral and 3D hexahedral meshes have been presented. These make the generation of high-quality structured meshes more robust and accessible for many important scientific computations and engineering designs and evaluations. Finally, techniques for processing and visualizing large-scale microvascular networks have been introduced. These techniques open the door to understanding the relationship between the change in microvascular networks and the progress of certain diseases, making the development of treatment for these diseases possible. 

     To make the techniques and frameworks resulting from this project accessible to researchers and other users in relevant fields, a client-server platform has been developed. This platform enables users to access these techniques using any modern web browser from their personal computers. These techniques will be deployed on the server side that supports programs and applications developed with different programming languages. 

     Some new analysis and visualization techniques developed by this project have been incorporated into the teaching of a visualization course at the University of Houston. These techniques include physics-based flow analysis and visualization, the study of the co-variant behaviors of different flow attributes, and the visualization of microvascular networks using graph representation. 

     The PI of this project has worked closely with domain experts who provide data for testing to ensure accurate results and assess the project's impact on relevant areas and disciplines, such as mechanical engineering, oceanography, and computational fluid dynamics. Furthermore, this project has supported the dissertation research of nine Ph.D. students, allowing them to contribute to the project's objectives and gain valuable research experience. Additionally, nine undergraduate students were sponsored through the Research Experiences for Undergraduates (REU) supplement of this project, providing them with opportunities to conduct research related to the project's goals and receive valuable training. As part of the project's outreach efforts, six local high school students were also given the chance to work with the project team on relevant research problems, furthering their interest and knowledge in STEM fields. 


 

 


Last Modified: 04/01/2023
Modified by: Guoning Chen

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