CAREER: Efficient Structural Analysis of Multivariate Fields for Scalable Visualization

可视化在研究和工业中起着至关重要的作用，它为用户提供了一个数据的图形界面，为他们提供了解释，评估和决策的直观基础。然而，所产生的科学数据的快速增长的大小、维度和多尺度复杂性产生了现有可视化技术没有适当解决的普遍分析挑战。特别是，固有的多变量和多场物理问题的调查通常减少到一个单一的标量或矢量场的可视化，从而忽略了丰富的重要信息。PI将通过开创一个全面的方法来解决当前最先进的大规模数据集的有效可视化分析的局限性。中央所提出的方法是一个新的定义几何显着性，将允许自动识别显着的流形在科学数据。为此，PI将统一和继承数学和计算机视觉中的各种概念，为多领域问题的结构分析和可视化表示创建一个有原则的通用模型。将设计利用并行架构的创新数据结构和稀疏采样策略，以实现大规模和多变量数据集的有效处理。最后，这些计算基础将为以用户为中心的可视化分析框架提供动力，PI将在跨流体动力学，材料工程，高能物理，这项研究工作将有利于科学界，为广泛的计算或测量数据集的有效分析和可视化提供严格和可扩展的框架。科学问题。PI将通过一个开源门户网站分发创建的软件工件，并将其集成到领先的可视化工具中，以促进其传播。PI将在主要会议上组织教程和研讨会，以提高用户社区对开发技术的认识，他将提供基准数据集和样本结果，以促进可视化社区的合作。除了研究，PI还将在本科和研究生阶段开设新课程，让学生了解数据分析在科学和工程中的重要性，以及高级可视化在这方面的作用。最后，这些教育活动将自然补充对代表性不足的少数民族和当地K-12计划的推广工作。

Visualization plays a crucial role in research and industry by offering users a graphical interface to their data that affords them an intuitive basis for interpretation, assessment and decision making. Yet, the rapidly growing size, dimensionality, and multi-scale complexity of the produced scientific data create a pervasive analysis challenge that is not properly addressed by existing visualization technology. In particular, the investigation of inherently multivariate and multifield physical problems is typically reduced to the visualization of a single scalar or vector field, thereby neglecting a wealth of important information.The PI will address the limitations of the current state of the art by pioneering a comprehensive approach for the efficient visual analysis of large-scale datasets. Central to the proposed approach is a novel definition of geometric saliency that will permit the automatic identification of remarkable manifolds in scientific data. To that end, the PI will unify and subsume a variety of concepts from mathematics and computer vision to create a principled and versatile model for the structural analysis and visual representation of multifield problems. Innovative data structures and sparse sampling strategies leveraging parallel architectures will be devised to enable the efficient processing of large and multivariate datasets at scale. Lastly, these computational foundations will power a user-centric visual analysis framework that the PI will assess in the context of multidisciplinary collaborations spanning fluid dynamics, materials engineering, high-energy physics, and cardiovascular research.This research effort will benefit the scientific community by contributing a rigorous and scalable framework for the effective analysis and visualization of computational or measured datasets across a broad range of scientific problems. The PI will distribute the created software artifacts through an open source web portal and integrate them in leading visualization tools to facilitate their dissemination. The PI will organize tutorials and workshops at premier conferences to raise the awareness of the user community about the developed technology and he will provide benchmark datasets and sample results to promote a collaborative effort in the visualization community. Beyond research, the PI will create new courses at both the undergraduate and graduate levels to expose students to the critical importance of data analysis in science and engineering and to the role of advanced visualization in this context. Finally, these education activities will naturally complement an outreach effort toward underrepresented minorities and local K-12 programs.