The evaluation of failure probability for complex engineering system is a computationally intensive task. While the Monte Carlo method is easy to implement, it converges slowly and therefore requires numerous repeated simulations of the failure model to generate sufficient samples. To improve the efficiency, methods based on surrogate models are proposed to approximate the limit state function. In this work, we reframe the approximation of the limit state function as an operator learning problem and utilize the DeepONet framework with a hybrid approach to estimate the failure probability. Numerical results show that our proposed method outperforms the prior neural hybrid method.

翻译：复杂工程系统的失效概率评估是一项计算密集型任务。虽然蒙特卡洛方法易于实现，但其收敛速度较慢，因此需要大量重复模拟失效模型以生成足够的样本。为提高效率，基于代理模型的方法被提出用于近似极限状态函数。本文中，我们将极限状态函数的近似重构为算子学习问题，并利用DeepONet框架结合混合方法估计失效概率。数值结果表明，我们提出的方法优于先前的神经混合方法。