Bayesian optimization for solving high-frequency passive component design problems

In this paper, the performance of the Bayesian Optimization (BO) technique applied to various problems of microwave engineering is studied. Bayesian optimization is a novel, non-deterministic, global optimization scheme that uses machine learning to solve complex optimization problems. However, each...

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Vydané v:Bulletin of the Polish Academy of Sciences. Technical sciences Ročník 70; číslo 4; s. 141595
Hlavní autori: Baranowski, Michal, Fotyga, Grzegorz, Lamecki, Adam, Mrozowski, Michal
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Warsaw Polish Academy of Sciences 2022
Predmet:
Algorithms
Antenna arrays
Antenna design
Antennas
Bayesian analysis
bayesian optimization
Circuit design
Design
Designers
Engineering
Filter design (mathematics)
Global optimization
high-frequency design
Machine learning
Methods
Microwave filters
Optimization techniques
Passive components
Radiation
Variables
ISSN:2300-1917, 0239-7528, 2300-1917
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Shrnutí:In this paper, the performance of the Bayesian Optimization (BO) technique applied to various problems of microwave engineering is studied. Bayesian optimization is a novel, non-deterministic, global optimization scheme that uses machine learning to solve complex optimization problems. However, each new optimization scheme needs to be evaluated to find its best application niche, as there is no universal technique that suits all problems. Here, BO was applied to different types of microwave and antenna engineering problems, including matching circuit design, multiband antenna and antenna array design, or microwave filter design. Since each of the presented problems has a different nature and characteristics such as different scales (i.e. number of design variables), we try to address the question about the generality of BO and identify the problem areas for which the technique is or is not recommended.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2300-1917
0239-7528
2300-1917
DOI:10.24425/bpasts.2022.141595