Fast Insight into High-Dimensional Parametrized Simulation Data
Fast Insight into High-Dimensional Parametrized Simulation Data
复制标题
快速洞察高维参数化仿真数据
DOI：
10.1109/icmla.2012.189
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
D. Pflüger
中科院分区：
文献类型：
--
作者：
D. Butnaru;B. Peherstorfer;H. Bungartz;D. Pflüger
在大多数工业产品开发过程中，数值模拟已成为一种不可避免的工具，模拟用于了解设计决策（参数配置）对产品结构和性能的影响。然而，为了让工程师彻底探索设计空间和微调参数，许多-通常非常耗时-仿真运行是必要的。此外，这导致大量的数据无法在没有适当工具的支持下以有效的方式进行分析。在本文中，我们解决了两个问题：第一，立即提供仿真结果，如果参数配置发生变化，第二，确定特定区域的设计空间集中变化，从而重要性。我们建议使用一个分层的方法，稀疏网格插值或回归的基础上，作为一个有效的和廉价的替代模拟。此外，我们开发了新的视觉表示的基础上固有的层次基础上所包含的衍生信息。即使在更高维度的设置中，它们也可以直观地让用户识别有趣的参数区域。该工作流程结合在一个交互式可视化和探索框架中。我们讨论了计算科学和工程的不同领域的例子，并展示了我们的基于稀疏网格的技术如何使参数依赖性变得明显，以及它们如何用于微调参数配置。
Numerical simulation has become an inevitable tool in most industrial product development processes with simulations being used to understand the influence of design decisions (parameter configurations) on the structure and properties of the product. However, in order to allow the engineer to thoroughly explore the design space and fine-tune parameters, many -- usually very time-consuming -- simulation runs are necessary. Additionally, this results in a huge amount of data that cannot be analyzed in an efficient way without the support of appropriate tools. In this paper, we address the two-fold problem: First, instantly provide simulation results if the parameter configuration is changed, and, second, identify specific areas of the design space with concentrated change and thus importance. We propose the use of a hierarchical approach based on sparse grid interpolation or regression which acts as an efficient and cheap substitute for the simulation. Furthermore, we develop new visual representations based on the derivative information contained inherently in the hierarchical basis. They intuitively let a user identify interesting parameter regions even in higher-dimensional settings. This workflow is combined in an interactive visualization and exploration framework. We discuss examples from different fields of computational science and engineering and show how our sparse-grid-based techniques make parameter dependencies apparent and how they can be used to fine-tune parameter configurations.
使用稀疏网格进行预计算仿真数据的高维计算引导
DOI：
10.1016/j.procs.2011.04.007
发表时间：
2011
期刊：
影响因子：
--
作者：
Butnaru;Pflüger;Bungartz
通讯作者：
Bungartz