Variational Methods for Model-based Interacitve Analysis of Flows

本项目旨在探索性分析和分类复杂的流动模式，如剪切摩擦、卷曲或漩涡。对于2D和3D的真实世界流及其时间演化，完全自动化和全局分类是遥不可及的。因此，我们将开发一种流探测方法，该方法将多尺度变分方法用于流型分类与灵活的用户引导界面相结合。变分泛函包括一个保真度项，它将观察到的流动与参数化的物理模型或统计学习模型进行比较，一个反映模式参数及其形状的概率的先验，以及一个编码用户指导和偏好的合适函数。所开发的分析工具将应用于由不同类型的图像序列呈现的实验流动数据和计算流体动力学模拟的流场。用户可以交互式地选择从全局宏观到局部细观流动现象的流动分类尺度。

This project aims at an explorative analysis and classification of complex flow pattern such as shear friction, curls or vortices. For real world flows in 2D and 3D and their temporal evolution a fully automatic and global classification is far out of reach. Hence, we will develop a flow exploration method which combines a multi-scale variational approach for the classification of flow pattern with a flexible interface for user guidance. The variational functional consists of a fidelity term, which compares the observed flow locally with parametrized physical or statistically learned models, a prior reflecting the probability of the pattern parameters and its shape, and a suitable functional encoding the user’s guidance and preferences. The analysis tool to be developed will be applied to experimental flow data rendered by different types of image sequences and to flow fields from computational fluid dynamics simulation. The user interactively selects the scale for the flow classification ranging from global macroscopic to local mesoscopic flow phenomena.