Models with high-dimensional parameter spaces are common in many applications. Global sensitivity analyses can provide insights on how uncertain inputs and interactions influence the outputs. Many sensitivity analysis methods face nontrivial challenges for computationally demanding models. Common approaches to tackle these challenges are to (i) use a computationally efficient emulator and (ii) sample adaptively. However, these approaches still involve potentially large computational costs and approximation errors. Here we compare the results and computational costs of four existing global sensitivity analysis methods applied to a test problem. We sample different model evaluation time and numbers of model parameters. We find that the emulation and adaptive sampling approaches are faster than Sobol' method for slow models. The Bayesian adaptive spline surface method is the fastest for most slow and high-dimensional models. Our results can guide the choice of a sensitivity analysis method under computational resources constraints.
翻译:具有高维参数空间的模型在许多应用中很常见。全球敏感度分析可以提供对不确定的投入和相互作用如何影响产出的洞察力。许多敏感度分析方法在计算要求高的模型中面临非三重挑战。应对这些挑战的共同方法是:(一) 使用计算效率高的模拟器和(二) 随机抽样。然而,这些方法仍然涉及潜在的巨大计算成本和近似误差。我们在这里比较适用于测试问题的四种现有全球敏感度分析方法的结果和计算成本。我们抽样抽样调查了不同的模型评价时间和模型参数的数目。我们发现模拟和适应性取样方法比慢速模型的Sobol方法更快。对于最慢和高维模型来说,Bayesian适应性样板表法是最快的。我们的成果可以指导计算资源限制下的敏感度分析方法的选择。</s>