LEAM: A Prompt-only Large Language Model-enabled Antenna Modeling Method
This addresses a domain-specific problem for antenna designers by speeding up analysis and design, though it appears incremental as it applies existing LLM technology to a new application area.
The paper tackles the time-consuming and complex process of antenna modeling by introducing LEAM, a method that uses large language models to automatically generate antenna models from various inputs like descriptions, images, or academic sources, achieving correct models in minutes for three tested examples.
Antenna modeling is a time-consuming and complex process, decreasing the speed of antenna analysis and design. In this paper, a large language model (LLM)- enabled antenna modeling method, called LEAM, is presented to address this challenge. LEAM enables automatic antenna model generation based on language descriptions via prompt input, images, descriptions from academic papers, patents, and technical reports (either one or multiple). The effectiveness of LEAM is demonstrated by three examples: a Vivaldi antenna generated from a complete user description, a slotted patch antenna generated from an incomplete user description and the operating frequency, and a monopole slotted antenna generated from images and descriptions scanned from the literature. For all the examples, correct antenna models are generated in a few minutes. The code can be accessed via https://github.com/TaoWu974/LEAM.