GV: Small: Collaborative Research: Analysis and Visualization of Stochastic Simulation Solutions

GV：小型：协作研究：随机仿真解决方案的分析和可视化

• 批准号: 0914447
• 负责人: Dongbin Xiu
• 金额: $ 22.25万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0914447&HistoricalAwards=false
• 关键词: GV; Small; Collaborative; Research; Analysis

This is a collaborative research project conducted by Robert Kirby, University of Utah (IIS-0914564) and Dongbin Xiu, Purdue University (IIS-0914447). In this age of scientific computing, the simulation science pipeline of mathematical modeling, simulation and evaluation is a commonly employed rendition of the scientific method. In addition to the traditional components of the pipeline, there has been a recent surge of interest in uncertainty quantification (UQ). Visualization is the window through which scientists examine their data for deriving new science, and hence visualization methods which depict underlying uncertainty information are crucial. This research addresses the questions of how does one accurately and efficiently post-process stochastic simulation fields and how does one effectively and succinctly convey the results. This is accomplished by developing strategies and techniques for augmenting current visualization techniques used for visualizing spatio-temporal fields with UQ information in a seamless way. The broader impacts of this work are that (1) proper techniques for UQ will have large impact on many scientific disciplines from medical/bioengineering to aeronautics, and (2) developed visualization techniques might be put to use when higher dimensional data is available for each point in space. The educational objectives are focused on training a new generation of scientists who are proficient not in both visualization techniques and in UQ. The project will produce a series of methods and algorithms for stochastic visualization. These pioneering results will be disseminated in archival publications as well as via the project website (http://www.cs.utah.edu/~kirby/StochasticVis.html). Workshops on stochastic methods and tutorial sessions in SIAM and IEEE conferences are also planned to raise the visibility and impact of the project.

这是犹他大学Robert Kirby （IIS-0914564）和普渡大学Dongbin Xiu （IIS-0914447）的合作研究项目。在这个科学计算的时代，数学建模、仿真和评估的仿真科学流水线是科学方法的一种常用表现形式。除了管道的传统组成部分外，最近对不确定性量化（UQ）的兴趣激增。可视化是科学家检验数据以获得新科学的窗口，因此描述潜在不确定性信息的可视化方法至关重要。本研究解决了如何准确、高效地对随机模拟场进行后处理以及如何有效、简洁地传达结果的问题。这是通过开发策略和技术来实现的，这些策略和技术可以增强当前用于以无缝方式可视化UQ信息的时空场的可视化技术。这项工作的更广泛的影响是：(1)UQ的适当技术将对从医学/生物工程到航空学的许多科学学科产生重大影响，(2)当空间中的每个点都可以获得更高维度的数据时，开发的可视化技术可能会被使用。教育目标的重点是培养既精通可视化技术又精通UQ的新一代科学家。该项目将产生一系列随机可视化的方法和算法。这些开创性的成果将在档案出版物中以及通过项目网站（http://www.cs.utah.edu/~kirby/StochasticVis.html）传播。还计划在SIAM和IEEE会议上举办关于随机方法的讲习班和指导会议，以提高项目的知名度和影响。