Sobol's method is one of the most powerful and widely used frameworks for global sensitivity analysis, and it maps every possible combination of input variables to an associated Sobol index. However, these indices are often challenging to analyze in depth, due in part to the lack of suitable, flexible enough, and fast-to-query data access structures as well as visualization techniques. We propose a visualization tool that leverages tensor decomposition, a compressed data format that can quickly and approximately answer sophisticated queries over exponential-sized sets of Sobol indices. This way, we are able to capture the complete global sensitivity information of high-dimensional scalar models. Our application is based on a three-stage visualization, to which variables to be analyzed can be added or removed interactively. It includes a novel hourglass-like diagram presenting the relative importance for any single variable or combination of input variables with respect to any composition of the rest of the input variables. We showcase our visualization with a range of example models, whereby we demonstrate the high expressive power and analytical capability made possible with the proposed method.

中文翻译：

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SenVis：基于张量的交互式灵敏度可视化

Sobol 的方法是用于全局敏感性分析的最强大和最广泛使用的框架之一，它将输入变量的每种可能组合映射到相关的 Sobol 指数。然而，这些索引通常难以深入分析，部分原因是缺乏合适、足够灵活且快速查询的数据访问结构以及可视化技术。我们提出了一种利用张量分解的可视化工具，这是一种压缩数据格式，可以快速、近似地回答对指数大小的 Sobol 索引集的复杂查询。通过这种方式，我们能够捕获高维标量模型的完整全局灵敏度信息。我们的应用程序基于三阶段可视化，可以交互地添加或删除要分析的变量。它包括一个新颖的类似沙漏的图表，展示了任何单个变量或输入变量组合相对于其余输入变量的任何组合的相对重要性。我们通过一系列示例模型展示了我们的可视化，从而展示了所提出的方法所具有的高表达能力和分析能力。