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CAREER: A Measure Theoretic Framework for Topology-Based Visualization

职业生涯：基于拓扑的可视化的测量理论框架

基本信息

批准号：
2145499
负责人：
Bei Phillips
金额：
$ 59.94万
依托单位：
University of Utah
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-15 至 2027-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145499&HistoricalAwards=false
关键词：
CAREER Measure Theoretic Framework Topology

项目摘要

Data generated from multiphysics simulations, such as binary black hole mergers and fluid dynamics, have experienced exponential growth because of the growing capabilities of computing facilities. At the same time, data-intensive science relies on the acquisition, management, analysis, and visualization of data with increasing spatial and temporal resolutions. This project develops a new set of approaches to support the core tasks in scientific data visualization (such as feature tracking, event detection, ensemble analysis, and interactive visualization) in a way that is more reflective of the underlying physics using measure theory. The results will be instantiated by a collection of open-source software tools to be deployed for the collaborating scientists in materials science and high-performance computing, and the larger research community. This project leverages tools from geometric measure theory, information theory, and transportation theory for topology-based visualization, which utilizes topological concepts to describe, reduce and organize data for scientific understanding and communication. The project focuses on two technical components. The first component represents topological descriptors as metric spaces equipped with probability measures, which supports their enrichments with physical quantities, information quantification, and comparative analysis. The second component uses information and transportation theory to enable a wide variety of visualization tasks for time-varying data and ensembles. The project couples correspondence criteria with optimization processes from optimal transport to understand the evolution of features of interest; incorporates uncertainty in event detection with geometric measures; as well as utilizes statistics of metric measure spaces to guide interactive visualization. The investigator works closely with scientists using data from astrophysics, materials science, and mechanical engineering to evaluate and tune the framework to better reflect the underlying physics. This project provides a unique environment for multidisciplinary activities and training opportunities for undergraduate and graduate students.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

多物理场模拟产生的数据，如双黑洞合并和流体动力学，经历了指数增长，因为越来越多的计算设施的能力。与此同时，数据密集型科学依赖于数据的获取、管理、分析和可视化，并具有越来越高的空间和时间分辨率。该项目开发了一套新的方法来支持科学数据可视化的核心任务（如特征跟踪，事件检测，集成分析和交互式可视化），其方式更能反映使用测量理论的基础物理学。这些结果将通过一系列开源软件工具来实例化，这些工具将部署给材料科学和高性能计算领域的合作科学家以及更大的研究社区。该项目利用几何测量理论，信息论和运输理论的工具进行基于拓扑的可视化，利用拓扑概念来描述，减少和组织数据以进行科学理解和交流。该项目侧重于两个技术组成部分。第一个组成部分表示拓扑描述符作为度量空间配备了概率措施，这支持他们的丰富的物理量，信息量化和比较分析。第二个组件使用信息和传输理论，使各种各样的可视化任务的时变数据和合奏。该项目将通信标准与最佳运输的优化过程相结合，以了解感兴趣特征的演变;将事件检测中的不确定性与几何测量相结合;以及利用度量测量空间的统计数据来指导交互式可视化。研究人员与科学家密切合作，使用来自天体物理学，材料科学和机械工程的数据来评估和调整框架，以更好地反映基础物理学。该项目为本科生和研究生的多学科活动和培训机会提供了一个独特的环境。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。