论文标题
使用混合高斯过程回归和遗传多目标方法优化产量
Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach
论文作者
论文摘要
不确定性的量化和最小化是电磁设备设计中的重要任务，这带有高度计算工作。我们提出了一种混合方法，将蒙特卡洛分析的可靠性和准确性与基于高斯过程回归的替代模型的效率相结合。我们提出两种优化方法。一种自适应牛顿-MC，可减少不确定性和遗传多目标方法的影响，以优化性能和鲁棒性。对于用作基准问题的电介质波导，所提出的方法的表现优于经典方法。
Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.
