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• W3092429931 abstract "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." @default.
• W3092429931 created "2020-10-15" @default.
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• W3092429931 date "2021-12-17" @default.
• W3092429931 modified "2023-10-04" @default.
• W3092429931 title "Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach" @default.
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• W3092429931 doi "https://doi.org/10.5194/ars-19-41-2021" @default.
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