CAMERA：一种费用感知、自适应、多保真度、高效的可靠性分析方法
CAMERA: A Method for Cost-aware, Adaptive, Multifidelity, Efficient Reliability Analysis
摘要：
估算航空航天系统的故障概率是飞行认证和鉴定的关键要求。失效概率估计涉及到解决概率分布的尾部，当必须查询昂贵的高保真模拟时，蒙特卡罗抽样方法是难以处理的。我们提出了一种方法来使用贸易的准确性为计算效率的多个并行模型。具体来说，我们建议使用多保真度高斯过程模型，以有效地融合模型在多个保真度，从而提供了一个廉价的代理模型，模拟原始模型在所有的qualityities。此外，我们提出了一种新的顺序\emph{acquisition function}为基础的实验设计框架，可以自动选择样本从适当的保真度模型，使预测量的兴趣在最高的保真度。我们使用我们提出的方法在一个重要的抽样设置，并证明我们的方法的故障水平集估计和概率估计的综合检验函数，以及两个现实世界的应用，即，可靠性分析的燃气涡轮发动机叶片使用有限元法和跨音速气动机翼测试情况下，使用雷诺平均Navier-斯托克斯方程。我们证明，我们的方法预测故障边界和概率更准确，计算效率更高，同时使用不同的保真度模型相比，只使用一个昂贵的高保真模型。
英文摘要：
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distribution, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. We propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential \emph{acquisition function}-based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest in the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set estimation and probability estimation on synthetic test functions as well as two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier--Stokes equations. We demonstrate that our method predicts the failure boundary and probability more accurately and computationally efficiently while using varying fidelity models compared with using just a single expensive high-fidelity model.
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