By their very nature, rare event probabilities are expensive to compute; they are also delicate to estimate as their value strongly depends on distributional assumptions on the model parameters. Hence, understanding the sensitivity of the computed rare event probabilities to the hyper-parameters that define the distribution law of the model parameters is crucial. We show that by (i) accelerating the calculation of rare event probabilities through subset simulation and (ii) approximating the resulting probabilities through a polynomial chaos expansion, the global sensitivity of such problems can be analyzed through a double-loop sampling approach. The resulting method is conceptually simple and computationally efficient; its performance is illustrated on a subsurface flow application and on an analytical example.

中文翻译：

---

使用子集模拟和多项式混沌扩展的罕见事件概率的全局敏感性分析

就其本质而言，稀有事件概率的计算成本很高。它们也很容易估计，因为它们的值很大程度上取决于模型参数的分布假设。因此，了解计算的罕见事件概率对定义模型参数分布规律的超参数的敏感性至关重要。我们表明，通过（i）通过子集模拟加速稀有事件概率的计算和（ii）通过多项式混沌扩展来近似得到的概率，可以通过双循环采样方法分析此类问题的全局敏感性。由此产生的方法在概念上简单且计算效率高；其性能在地下流动应用程序和分析示例中进行了说明。