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Strategies for feature-assisted development of topology agnostic planar antennas using variable-fidelity models
Abstract
Design of antennas for contemporary applications presents a complex challenge that integrates cognitive-driven topology development with the meticulous adjustment of parameters through rigorous numerical optimization. Nevertheless, the process can be streamlined by emphasizing the automatic determination of structure geometry, potentially reducing the reliance on traditional methods that heavily rely on engineering insight in the course of antenna development. In this work, which is an extension of our conference paper [1], a specification-oriented design of topologically agnostic antennas is considered by utilizing two strategies focused on bandwidth-specific design and bandwidth-enhanced optimization. The process is embedded within a variable-fidelity framework, where the low-fidelity optimization involves classification of randomly generated topologies, followed by their local tuning using a trust-region algorithm applied to a feature-based representation of structure response. The final result is then tuned using just a handful of high-fidelity simulations. The strategies under consideration were verified on a case study basis concerning automatic generation of three radiators with varying parameters. Benchmarks of the algorithm against more standard optimization methods, as well as comparisons of the obtained topologies with respect to state-of-the-art antennas from literature have also been considered.
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Authors (5)
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Tom Dhaene
- Ghent University, IDLab
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Ivo Couckuyt
- Ghent University, IDLab
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Journal of Computational Science
no. 85,
ISSN: 1877-7503 - Language:
- English
- Publication year:
- 2025
- Bibliographic description:
- Bekasiewicz A., Askaripour K., Dzwonkowski M., Dhaene T., Couckuyt I.: Strategies for feature-assisted development of topology agnostic planar antennas using variable-fidelity models// Journal of Computational Science -, (2025), s.102521-
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.jocs.2024.102521
- Sources of funding:
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- Free publication
- Verified by:
- Gdańsk University of Technology
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