Merih Palandöken, Aysu Belen, Ozlem Tari, Peyman Mahouti, Tarlan Mahouti, Mehmet A. Belen
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引用次数: 0
Abstract
In this paper, deep learning–based data-driven surrogate modeling approach is proposed for enhancing cost-efficiency of multiband antenna design optimization. The proposed surrogate model–assisted design approach has achieved a computational cost reduction of almost 40% compared to the conventional direct electromagnetic solver–based design methodologies in case of single design example. As for the validation of the proposed method, the obtained optimal design parameters from the surrogate model are used to manufacture an antenna design. The obtained results from the experimental measurement are compared with counterpart results from the literature.
期刊介绍：
International Journal of RF and Microwave Computer-Aided Engineering provides a common forum for the dissemination of research and development results in the areas of computer-aided design and engineering of RF, microwave, and millimeter-wave components, circuits, subsystems, and antennas. The journal is intended to be a single source of valuable information for all engineers and technicians, RF/microwave/mm-wave CAD tool vendors, researchers in industry, government and academia, professors and students, and systems engineers involved in RF/microwave/mm-wave technology.
Multidisciplinary in scope, the journal publishes peer-reviewed articles and short papers on topics that include, but are not limited to. . .
-Computer-Aided Modeling
-Computer-Aided Analysis
-Computer-Aided Optimization
-Software and Manufacturing Techniques
-Computer-Aided Measurements
-Measurements Interfaced with CAD Systems
In addition, the scope of the journal includes features such as software reviews, RF/microwave/mm-wave CAD related news, including brief reviews of CAD papers published elsewhere and a "Letters to the Editor" section.
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