A Graph Neural Network-Based Electric-Field Prediction Model for Exposure Assessments

Grasping the electric-field (E-field) distribution is challenging yet important for monitoring the electromagnetic field (EMF) exposure level. In this paper, a graph neural network (GNN)-based prediction model is proposed to estimate the E-field distribution in a complex indoor environment. The model provides a good accuracy and has an appealing feature of being quite efficient.