Scalable deep bayesian optimization for antenna design with high degrees of freedom

Abstract

We propose a novel, scalable deep Bayesian optimization (BO) methodology for designing antennas with a large number of design degrees of freedom. Conventional BO approaches in antenna design have relied on Gaussian process (GP) surrogates, which limits its scalability to higher-dimensional design spaces. To address this limitation, we propose (1) a deep neural network (DNN) surrogate with Monte Carlo (MC) dropout for efficient multi-output Bayesian inference, (2) an active learning strategy to construct an informative initial dataset, and (3) a hybrid expected improvement–differential evolution (EI-DE) acquisition scheme balancing global exploration with local exploitation for efficient sample selection. Applied to a 52-variable ultra-wideband (UWB) antenna design scenario, the proposed method achieves a 75% reduction in computational cost compared to a conventional DE algorithm while achieving similar solution quality. It also outperforms some existing surrogateassisted optimizers, reducing computation time by over 40% while yielding superior designs. The proposed deep learning driven BO framework offers a promising direction for antenna synthesis.