Data Augmentation Approach With CVAE and DML Method for Antenna Modelling

Abstract

Full-wave electromagnetic simulators, such as HFSS and CST, are essential in antenna design and analysis, but their high computational resources and time cost often limit the amount of training data available for building surrogate models. To enhance modelling accuracy, particularly in contexts where the constraint of limited data arises, this study proposes a data augmentation (DA) framework that integrates a conditional variational autoencoder (CVAE) with deep metric learning (DML), where a contrastive loss is employed to ensure the quality of the synthetic samples. This approach is experimentally validated on the proposed wideband circularly polarized S-shaped slot antenna (WB-SSA), where the surrogate model is built as an inverse design, mapping multi-objective performances, including S₁₁, axial ratio (AR), and gain, to the corresponding structural parameters. Specifically, the CVAE encoder learns to map the structural parameter sets to a latent distribution, conditioned on the multi-objective performances. A contrastive loss regularizes this latent space by separating latent vectors with divergent multi-objective performances. The decoder, in turn, generates synthetic samples from randomly generated multi-objective vectors, thereby producing more reliable synthetic samples for DA application. Experimental results demonstrate that the proposed method contributes to a clear improvement in the performance of the inverse model.