A Deep Learning Convolutional Neural Network for Antenna Near-Field Prediction and Surrogate Modeling

Abstract

This study investigates the use of deep learning techniques for building a generalized surrogate model that can accurately and very efficiently predict antenna performance parameters. Notably, we focus on applications where a substantial amount of simulation time is required and prior data is available for deep learning use. Specifically, for these applications, we introduce deep learning models that efficiently and reliably model the near-field of the antenna. These models, in turn, accurately predict far-field properties and essential antenna metrics, such as the reflection coefficient. To demonstrate the efficiency of our method, the widely used rectangular patch antenna is considered, encompassing variations in several important geometrical parameters, dielectric constant, and frequency. Based on our results, the proposed model, once trained, is over 200 times faster than conventional full-wave simulations with a nominal average root mean square error (RMSE) of 0.0174 in predicting all the necessary antenna parameters, such as resonant frequency, radiation pattern, and directivity.

Keywords

• Antennas
• electromagnetics
• deep learning
• surrogate modeling