LAUSR

Deep-learning-assisted antenna design methods such as surrogate models have gained significant popularity in recent years due to their potential to greatly increase design efficiencies by replacing the time-consuming full-wave electromagnetic (EM) simulations. A large number of training data with sufficiently diverse and representative samples (antenna structure parameters, scattering properties, etc.) is mandatory for these methods to ensure good performance. However, traditional antenna modeling methods relying on manual model construction and modification are time-consuming and cannot meet the requirement of efficient training data acquisition. Also, automatic model construction methods are rarely studied. In this article, we pioneer investigation into the antenna model construction problem and first propose a deep-learning-assisted and image-based approach for achieving automatic model construction. Specifically, our method only needs an image of the antenna structure, usually available in scientific publications, as the input while the corresponding modeling codes (visual basic for application (VBA) language) are generated automatically. The proposed model mainly consists of two parts: convolutional neural network (CNN) and long short-term memory (LSTM) networks. The former is used for capturing features of antenna structure images and the latter is employed to generate the modeling codes. Experimental results show that the proposed method can automatically achieve the antenna parametric model construction with an overhead of approximately 50 s, which is a significant time reduction to manual modeling. The proposed parametric model construction method lays the foundation for further data acquisition, tuning, analysis, and optimization.