Advances in Radio Science
Advances in Radio Science
ARS
Articles | Volume 19
Articles | Volume 19
https://doi.org/10.5194/ars-19-41-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ars-19-41-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19
17 Dec 2021
 | 17 Dec 2021

Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach

Mona Fuhrländer and Sebastian Schöps

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Short summary
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. Further, we present a genetic multi-objective approach to optimize performance and robustness of a device at the same time.
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