Estimating the probability of failure for expensive simulations is a central task in reliability analysis for structural design, power grid design, and safety certification, among other areas. This work derives credible intervals on the probability of failure by modeling the simulation as a realization of a Gaussian process surrogate. These intervals are governed by the pointwise binary classification error of the surrogate and are compatible with the broad class of adaptive sampling schemes proposed in the literature. We further propose a novel batch sampling scheme that suggests multiple evaluation points per iteration, enabling parallel simulation on HPC systems. The method is empirically validated using our scalable, open-source implementation on a variety of test problems including a Tsunami model where failure is quantified in terms of maximum wave height.

翻译：在结构设计、电网设计及安全认证等领域的可靠性分析中，对高成本仿真的失效概率进行估计是一项核心任务。本研究通过将仿真建模为高斯过程代理模型的一个实现，推导出失效概率的可信区间。这些区间由代理模型的逐点二元分类误差所决定，并与文献中提出的广泛类别自适应采样方案兼容。我们进一步提出一种新颖的批量采样方案，该方案可在每次迭代中推荐多个评估点，从而实现在高性能计算系统上的并行仿真。该方法通过我们在多种测试问题（包括以最大波高量化失效的海啸模型）上可扩展的开源实现进行了实证验证。