EAGER: Define and Construct an Enhanced Graph Representation for Multiscale Vector Field Data Summarization

EAGER：定义和构建多尺度矢量场数据汇总的增强图形表示

• 批准号: 1352722
• 负责人: Guoning Chen
• 金额: $ 15万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1352722&HistoricalAwards=false
• 关键词: EAGER; Define; Construct; Enhanced; Graph

Vector field data analysis is indispensable for many applications in science and engineering, ranging from climate study, physics, chemistry, automobile design, to medical practice. Most existing analysis techniques for vector field data are not scalable to the real-world data with ever-increasing sizes and complexity. More importantly, the inherent limited visual perception channel largely constrains the ability to understand the complex geometric and physical behaviors of vector fields as a whole or in detail. To address these challenges, this exploratory project investigates a graph-based vector field data reduction for the subsequent extraction of a multi-scale vector field data summary. The summary serves as a condensed, yet informative, representation of the original vector field, supporting data interpretation and interaction and shielding the user from the underlying complexity of the flow dynamics. The key to computing such a summary representation is the construction of a novel, enhanced graph representation that encodes both the global structural information and local characteristics of the vector field, as well as other derived information. The approach focuses on development and validation of critical issues in graph-based vector field data reduction , including; (1) identification of the key information of a vector field for the construction of the enhanced graph: (2) efficient storage of the graph; and (3) new graph algorithms for extracting features of interest from the obtained graph. To address these issues, theories and algorithms from dynamical system, algebraic topology, tensor calculus, information theory, and graph theory are extended and integrated in a novel framework. To validate the approach, the PI is working closely with domain scientists from mechanical engineering and aerodynamics to receive advice on the representation of the summary and its utility in specific applications. The expected results in vector field summary represents will yield an important addition to the existing summarization techniques for various data forms. The analysis and abstraction are based on the enhanced graph and can enrich the conventional graph theory and graph algorithms. The ability to handle both steady and unsteady vector fields improves the theory and practice of dynamical systems in describing fluid dynamic phenomena, benefiting a wide variety of disciplines. Knowledge learned from the vector field summarization can be adapted to the study of summarized representation of more complex geometric data, such as tensor field data. In addition, the research on vector field summary represents one step towards a unified framework of knowledge discovery and integrity from heterogeneous data forms. The developed techniques are expected to be implemented as a software tool that will be applicable in a wider range of scientific and engineering domains. Furthermore, the new theory stemming from this work is expected to enrich the existing education on data analysis and visualization, enabling the development of new courses at both undergraduate and graduate levels in many academic disciplines. The project web site (http://www2.cs.uh.edu/~chengu/vf_summary/vf_summary.html) will provide access to project results, including developed software tools.

矢量场数据分析在从气候研究、物理、化学、汽车设计到医疗实践的许多科学和工程应用中都是不可或缺的。大多数现有的矢量场数据分析技术不能扩展到大小和复杂性不断增加的真实世界数据。更重要的是，固有的有限的视觉感知通道在很大程度上限制了对矢量场复杂的几何和物理行为的整体或细节的理解能力。为了解决这些挑战，这个探索性项目研究了一种基于图形的矢量场数据约简方法，用于后续提取多尺度矢量场数据摘要。摘要作为原始矢量场的精简但信息量丰富的表示，支持数据解释和交互，并使用户免受流动动力学的潜在复杂性的影响。计算这种汇总表示的关键是构造一种新颖的、增强的图形表示，该图形表示编码矢量场的全局结构信息和局部特征以及其他派生信息。该方法着重于基于图的矢量场数据约简中关键问题的开发和验证，包括：(1)识别用于构造增强图的矢量场的关键信息；(2)图的有效存储；(3)从获得的图中提取感兴趣的特征的新的图算法。为了解决这些问题，将动力系统、代数拓扑学、张量演算、信息论和图论的理论和算法扩展和集成到一个新的框架中。为了验证这种方法，PI正在与机械工程和空气动力学领域的科学家密切合作，以获得关于摘要的表示及其在特定应用中的实用性的建议。向量场汇总表示的预期结果将产生对各种数据形式的现有汇总技术的重要补充。分析和抽象是在增强图的基础上进行的，丰富了传统图论和图算法。同时处理稳定和非稳定矢量场的能力改进了动力系统在描述流体动力学现象方面的理论和实践，使各种学科受益。从矢量场汇总中学到的知识可以适用于更复杂几何数据的汇总表示的研究，例如张量场数据。此外，向量场摘要的研究代表着向异质数据表单中的知识发现和完整性的统一框架迈进了一步。预计所开发的技术将作为一种软件工具应用于更广泛的科学和工程领域。此外，这项工作产生的新理论预计将丰富现有的数据分析和可视化教育，使许多学科的本科生和研究生都能开发新的课程。项目网站(http://www2.cs.uh.edu/~chengu/vf_summary/vf_summary.html)将提供对项目成果的访问，包括开发的软件工具。

项目成果

An integral curve attribute based flow segmentation

基于积分曲线属性的流量分割

• DOI: 10.1007/s12650-015-0336-4
• 发表时间: 2016
• 期刊: Journal of Visualization
• 影响因子: 1.7
• 作者: Zhang, Lei;Laramee, Robert S.;Thompson, David;Sescu, Adrian;Chen, Guoning
• 通讯作者: Chen, Guoning

Compute and Visualize Discontinuity Among Neighboring Integral Curves of 2D Vector Fields

计算并可视化 2D 矢量场相邻积分曲线之间的不连续性

• DOI: 10.1007/978-3-319-44684-4_11
• 发表时间: 2017
• 期刊: Topological Methods in Data Analysis and Visualization
• 作者: Lei Zhang, Robert S.

Morse Decomposition of 3D Piecewise Linear Vector Fields

3D 分段线性矢量场的莫尔斯分解

• DOI: 10.2352/issn.2470-1173.2016.1.vda-477
• 发表时间: 2016
• 期刊: Electronic Imaging
• 作者: Berenjkoub, Marzieh;Chen, Guoning
• 通讯作者: Chen, Guoning

Hexahedral mesh re-parameterization from aligned base-complex

• DOI: 10.1145/2766941
• 发表时间: 2015-07
• 期刊: ACM Transactions on Graphics (TOG)
• 作者: Xifeng Gao;Z. Deng;Guoning Chen

Flow Visualization Based on A Derived Rotation Field

• DOI: 10.2352/issn.2470-1173.2016.1.vda-478
• 发表时间: 2016
• 作者: Lei Zhang;Guoning Chen;R. Laramee;D. Thompson;A. Sescu