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

Fuhrländer, Mona ; Schöps, Sebastian (2022):
Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach. (Publisher's Version)
In: Advances in Radio Science, 19, pp. 41-48. Copernicus Publications, e-ISSN 1684-9973,
DOI: 10.26083/tuprints-00021135,
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Item Type: Article
Origin: Secondary publication via sponsored Golden Open Access
Status: Publisher's Version
Title: Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach
Language: English
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.

Journal or Publication Title: Advances in Radio Science
Volume of the journal: 19
Place of Publication: Darmstadt
Publisher: Copernicus Publications
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields > Computational Electromagnetics
18 Department of Electrical Engineering and Information Technology > Institute for Accelerator Science and Electromagnetic Fields
Date Deposited: 13 Apr 2022 12:18
Last Modified: 23 Aug 2022 06:47
DOI: 10.26083/tuprints-00021135
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-211353
Additional Information:

Special issue statement: This article is part of the special issue “Kleinheubacher Berichte 2020”.

URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21135
PPN: 493053808
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