Reliability-based design optimization (RBDO) is traditionally formulated as a nested optimization and reliability problem. Although surrogate models are generally employed to improve efficiency, the approach remains computationally prohibitive in high-dimensional settings. This paper proposes a novel RBDO framework based on a stochastic simulator viewpoint, in which the deterministic limit-state function and the uncertainty in the model inputs are combined into a unified stochastic representation. Under this formulation, the system response conditioned on a given design is modeled directly through its output distribution, rather than through an explicit limit-state function. Stochastic emulators are constructed in the design space to approximate the conditional response distribution, enabling the semi-analytical evaluation of failure probabilities or associated quantiles without resorting to Monte Carlo simulation. Two classes of stochastic emulators are investigated, namely generalized lambda models and stochastic polynomial chaos expansions. Both approaches provide a deterministic mapping between design variables and reliability constraints, which breaks the classical double-loop structure of RBDO and allows the use of standard deterministic optimization algorithms. The performance of the proposed approach is evaluated on a set of benchmark problems with dimensionality ranging from low to very high, including a case with stochastic excitation. The results are compared against a Kriging-based approach formulated in the full input space. The proposed method yields substantial computational gains, particularly in high-dimensional settings. While its efficiency is comparable to Kriging for low-dimensional problems, it significantly outperforms Kriging as the dimensionality increases.

翻译：可靠性设计优化（RBDO）传统上被表述为一个嵌套优化与可靠性问题的组合。尽管通常采用代理模型来提高效率，但在高维场景下，该方法在计算上仍然令人望而却步。本文提出了一种基于随机模拟器视角的新型RBDO框架，其中将确定性极限状态函数与模型输入中的不确定性合并为一个统一的随机表示。在此公式下，系统响应在给定设计条件下的建模直接通过其输出分布，而非通过显式的极限状态函数来实现。在设计空间中构建随机模拟器以逼近条件响应分布，从而无需借助蒙特卡洛模拟即可实现对失效概率或相关分位数的半解析评估。研究了两类随机模拟器，即广义lambda模型和随机多项式混沌展开。这两种方法均在设计变量与可靠性约束之间建立了确定性映射，打破了RBDO经典的嵌套双循环结构，并允许使用标准的确定性优化算法。通过一组维度从低到高的基准问题（包括一个含有随机激励的案例）评估了所提方法的性能。结果与在全输入空间中构建的基于克里金法的方法进行了对比。所提方法在计算上取得了显著优势，尤其是在高维场景下。虽然其在低维问题中的效率与克里金法相当，但随着维度增加，其性能显著优于克里金法。