Physics-Informed Graph Neural Networks for the Inverse Design of GHz Reconfigurable Antenna

Reconfigurable antennas, as a subclass of meta-surfaces, offer innovative and dynamic capabilities for wireless communication systems. Specifically, enabling radiation pattern reconfigurability allows for flexible beam steering through reverse-engineering of antenna parameters such as surface current distributions. In this work, we present a physics-informed machine learning model, leveraging fundamental physics such as Kirchhoff's current Law, to predict the switch configurations of 2-dimensional antenna arrays. We utilize a graph neural network (GNN) to effectively capture the spatial relationships between radio-frequency (RF) switches and antenna patches, closely emulating the antenna topology. Simulation results demonstrate that our approach successfully predicts switch configurations needed to generate complex far-field radiation patterns.