Skip‐Connected CNN Exploiting BNN Surrogate for Antenna Modelling

The combination of evolutionary algorithms with full‐wave electromagnetic (EM) simulation software is an effective method for antenna optimal design. However, the high computational cost of calling the full‐wave EM simulation hundreds or thousands of times makes it unacceptable in terms of design time. In recent research, machine learning (ML) methods have achieved considerable success as surrogate models replacing full‐wave EM simulation software. Among them, the selection and construction of surrogate models are key factors. This paper proposes a skip‐connected surrogate model based on convolutional neural network (CNN) and Bayesian neural network (BNN). Firstly, transfer learning is employed to combine the spatial feature extraction capability of CNN with the model uncertainty of BNN. Then, skip connections are used to compensate for the loss of original information caused by feature extraction layers. Experimental results of antennas modeling demonstrate that the proposed algorithm improves the prediction accuracy and fitting performance relative to BNN. Moreover, while enhancing prediction accuracy, it also reduces the prediction uncertainty of BNN.