标题： 探询蜘蛛：高维不确定性表示的有效方法 显示英文标题

标题： Sounding Spider: An Efficient Way for Representing Uncertainties in High Dimensions

摘要： 本文提出了一种基于以下方法的多维数据可视化方法：(i) 动画功能假设结果图(f-HOPs)；(ii)三维Kiviat图；以及(iii)数据声化。在不确定性量化(UQ)框架下，这种分析与标准统计分析工具（如概率密度函数(PDF)）结合使用，可用于增强对数值代码输入中的不确定性如何转化为感兴趣量(QoI)中的不确定性的理解。与功能最高密度区域(HDR)箱线图或功能箱线图等大多数先进方法的静态表示相比，f-HOPs是一种动态可视化，使从业者能够推断物理的动力学，并能观察到可能存在的函数相关性。尽管该技术仅允许表示QoI，我们提出了Kiviat图的三维版本来编码所有输入参数。这种新的可视化通过数据声化利用了来自f-HOPs的信息。总的来说，这使得在同一画布上分析高维参数空间内的大数据集和函数QoI成为可能。所提出的方法在两个相关的环境数据集上进行了评估，并展示了其优势。 显示英文摘要

摘要： This article proposes a visualization method for multidimensional data based on: (i) Animated functional Hypothetical Outcome Plots (f-HOPs); (ii) 3-dimensional Kiviat plot; and (iii) data sonification. In an Uncertainty Quantification (UQ) framework, such analysis coupled with standard statistical analysis tools such as Probability Density Functions (PDF) can be used to augment the understanding of how the uncertainties in the numerical code inputs translate into uncertainties in the quantity of interest (QoI). In contrast with static representation of most advanced techniques such as functional Highest Density Region (HDR) boxplot or functional boxplot, f-HOPs is a dynamic visualization that enables the practitioners to infer the dynamics of the physics and enables to see functional correlations that may exist. While this technique only allows to represent the QoI, we propose a 3-dimensional version of the Kiviat plot to encode all input parameters. This new visualization takes advantage of information from f-HOPs through data sonification. All in all, this allows to analyse large datasets within a high-dimensional parameter space and a functional QoI in the same canvas. The proposed method is assessed and showed its benefits on two related environmental datasets.