Variational Methods for Model-based Interacitve Analysis of Flows

基于模型的流量交互分析的变分方法

• 批准号: 81897381
• 负责人: Professor Dr. Daniel Cremers
• 金额: --
• 依托单位: Lehrstuhl für Informatik IX: Bildverarbeitung und Künstliche Intelligenz
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2014-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/81897381?language=en
• 关键词: Variational; Methods; Model; based; Interacitve

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.

该项目旨在对剪切摩擦、卷曲或涡流等复杂流型进行探索性分析和分类。对于现实世界的 2D 和 3D 流动及其时间演化，全自动的全局分类是遥不可及的。因此，我们将开发一种流探索方法，它将用于流模式分类的多尺度变分方法与用于用户指导的灵活界面相结合。变分函数由保真度项（将局部观察到的流与参数化物理或统计学习模型进行比较）、反映模式参数及其形状的概率的先验项以及编码用户指导和偏好的合适函数组成。即将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数据以及计算流体动力学模拟的流场。用户交互地选择流分类的尺度，范围从全局宏观到局部细观流现象。