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.

中文翻译：

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使用混合高斯过程回归和遗传多目标方法的产量优化

不确定性的量化和最小化是电磁设备设计中的一项重要任务，需要大量的计算工作。我们提出了一种混合方法，将蒙特卡罗分析的可靠性和准确性与基于高斯过程回归的代理模型的效率相结合。我们提出了两种优化方法。减少不确定性影响的自适应 Newton-MC 和同时优化性能和鲁棒性的遗传多目标方法。对于用作基准问题的介电波导，所提出的方法优于经典方法。