论文标题
用于多场数据集结构分析的混合Lagrangian-Eulerian模型
A Hybrid Lagrangian-Eulerian Model for the Structural Analysis of Multifield Datasets
论文作者
论文摘要
多场数据集在计算科学的大量研究和工程应用中很常见。相应数据集的有效可视化可以通过阐明描述现象物理学的属性之间存在的复杂而动态的相互作用来促进其分析。我们在本文中介绍了一种新的混合拉格朗日 - 欧拉尔模型，该模型将现有的Lagrangian可视化技术扩展到了多场问题的分析。特别是，我们在整个数据空间中揭示多场数据集结构的方法因素，从而结合了Eulerian和Lagrangian观点。我们在几种流体动力学应用的背景下评估我们的技术。我们的结果表明，我们提出的方法能够表征现有方法遗漏的重要结构特征。
Multifields datasets are common in a large number of research and engineering applications of computational science. The effective visualization of the corresponding datasets can facilitate their analysis by elucidating the complex and dynamic interactions that exist between the attributes that describe the physics of the phenomenon. We present in this paper a new hybrid Lagrangian-Eulerian model that extends existing Lagrangian visualization techniques to the analysis of multifields problems. In particular, our approach factors in the entire data space to reveal the structure of multifield datasets, thereby combining both Eulerian and Lagrangian perspectives. We evaluate our technique in the context of several fluid dynamics applications. Our results indicate that our proposed approach is able to characterize important structural features that are missed by existing methods.
