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

这是一个由犹他大学罗伯特·柯比(IIS-0914564)和普渡大学修冬斌(IIS-0914447)共同开展的研究项目。在这个科学计算的时代，数学建模、仿真和评估的模拟科学管道是科学方法的常见再现。除了管道的传统组成部分外，最近人们对不确定性量化(UQ)的兴趣激增。可视化是科学家检查他们的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。本研究解决的问题是如何准确而高效地对随机模拟场进行后处理，以及如何有效而简洁地传达结果。这是通过开发战略和技术来实现的，该战略和技术用于以无缝方式用UQ信息来增强用于可视化时空场的当前可视化技术。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，以及(2)当空间中的每个点都有更高维度的数据时，可能会使用开发的可视化技术。教育目标侧重于培养新一代的科学家，他们既不精通可视化技术，也不精通UQ。该项目将产生一系列随机可视化的方法和算法。这些开创性成果将在档案出版物以及项目网站(http://www.cs.utah.edu/~kirby/StochasticVis.html).上传播还计划在SIAM和IEEE会议上举办关于随机方法的讲习班和辅导会议，以提高该项目的知名度和影响力。

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.