论文标题
使用高斯工艺回归的黑框产量估计工作流程应用于电磁设备的设计
A Blackbox Yield Estimation Workflow with Gaussian Process Regression Applied to the Design of Electromagnetic Devices
论文作者
论文摘要
在本文中，提出了一种有效可靠的随机收益估计方法。由于不确定性定量的主要挑战是计算可行性，因此我们提出了一种混合方法，其中大多数蒙特卡洛样品点都用替代模型评估，并且只有几个样品点通过原始的高富达模型重新评估。高斯过程回归是一种非侵入性方法，用于构建替代模型。没有许多先决条件，这不仅为我们提供了函数值的近似值，还为我们可以用来确定是否应重新评估样品点的错误指标。对于两个基准问题，即介电波导和低通滤波器，该方法的表现要优于经典方法。
In this paper an efficient and reliable method for stochastic yield estimation is presented. Since one main challenge of uncertainty quantification is the computational feasibility, we propose a hybrid approach where most of the Monte Carlo sample points are evaluated with a surrogate model, and only a few sample points are reevaluated with the original high fidelity model. Gaussian Process Regression is a non-intrusive method which is used to build the surrogate model. Without many prerequisites, this gives us not only an approximation of the function value, but also an error indicator that we can use to decide whether a sample point should be reevaluated or not. For two benchmark problems, a dielectrical waveguide and a lowpass filter, the proposed methods outperform classic approaches.
