Sensitivity analysis plays an important role in the development of computer models/simulators through identifying the contribution of each (uncertain) input factor to the model output variability. This report investigates different aspects of the variance-based global sensitivity analysis in the context of complex black-box computer codes. The analysis is mainly conducted using two R packages, namely sensobol (Puy et al., 2021) and sensitivity (Iooss et al., 2021). While the package sensitivity is equipped with a rich set of methods to conduct sensitivity analysis, especially in the case of models with dependent inputs, the package sensobol offers a bunch of user-friendly tools for the visualisation purposes. Several illustrative examples are supplied that allow the user to learn both packages easily and benefit from their features.
翻译:敏感度分析通过确定每个(不确定)输入因素对模型输出变异性的贡献,在发展计算机模型/模拟器方面发挥了重要作用。本报告在复杂的黑盒计算机代码的背景下调查基于差异的全球敏感度分析的不同方面。分析主要使用两个R包进行,即Sensobol(Puy等人,2021年)和灵敏度(Iooss等人,2021年)。虽然软件包敏感度配有一套丰富的敏感度分析方法,特别是在有依赖性输入的模型中,但软件包传感器为可视化目的提供了一系列方便用户的工具。提供了几个示例,使用户能够轻松地学习软件包,并从中受益。