Making Sense of Scientific Simulation Ensembles With Semantic Interaction
Making Sense of Scientific Simulation Ensembles With Semantic Interaction
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DOI：
10.1111/cgf.14029
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发表时间：
2020-06-03
影响因子：
2.5
通讯作者：
Pollyea, R. M.
中科院分区：
文献类型：
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作者：
Dahshan, M.;Polys, N. F.;Pollyea, R. M.
在复杂物理系统的研究中，科学家使用模拟来研究不同模型和参数的影响。为了理解多个维度之间的影响和关系，他们通常会运行许多模拟，并在所谓的“整体”中改变初始条件。集合则是多个运行，每个运行都是多维和多变量的。为了了解模拟参数与输出数据模式之间的联系，我们一直在开发一种科学数据可视化分析方法，将人类的专业知识和直觉与机器学习和统计学相结合。我们的方法体现在一个新的可视化工具，GLEE（图形链接的集合资源管理器），它允许科学家探索，搜索，过滤和理解他们的集合。GLEE使用可视化和语义交互（SI）技术，使科学家能够找到运行之间的相似性和差异，找到不同参数之间的相关性，并探索不同运行和参数之间的关系和相关性。我们的方法支持科学家选择有趣的运行子集，以调查和总结的因素和统计数据，显示在不同的运行变化和重复。在本文中，我们与专家一起评估我们的工具，以了解其优化和逆问题的优势和劣势。
In the study of complex physical systems, scientists use simulations to study the effects of different models and parameters. Seeking to understand the influence and relationships among multiple dimensions, they typically run many simulations and vary the initial conditions in what are known as 'ensembles'. Ensembles are then a number of runs that are each multi-dimensional and multi-variate. In order to understand the connections between simulation parameters and patterns in the output data, we have been developing an approach to the visual analysis of scientific data that merges human expertise and intuition with machine learning and statistics. Our approach is manifested in a new visualization tool, GLEE (Graphically-Linked Ensemble Explorer), that allows scientists to explore, search, filter and make sense of their ensembles. GLEE uses visualization and semantic interaction (SI) techniques to enable scientists to find similarities and differences between runs, find correlation(s) between different parameters and explore relations and correlations across and between different runs and parameters. Our approach supports scientists in selecting interesting subsets of runs in order to investigate and summarize the factors and statistics that show variations and consistencies across different runs. In this paper, we evaluate our tool with experts to understand its strengths and weaknesses for optimization and inverse problems.