Enhanced Deep Learning Approach for Electromagnetic Forward Modeling of Dielectric Target Within the Wide Frequency Band Using Deep Residual Convolutional Neural Network

This letter proposes a novel deep learning (DL)-based fast full-wave broad-band solver to realize electromagnetic forward (EMF) modeling for 2-D dielectric target. This DL-based EMF solver is based on the complex-valued deep residual convolutional neural network (DRCNN), which employs the residual block and deep convolutional operation to improve its generality and performance. While the input of DRCNN combines the incident electromagnetic (EM) field at the arbitrary frequency within the extremely-broad frequency band with the contrasts distribution of the region-of-interest (ROI), the corresponding output is the total EM field illuminated by the input incident EM field. The training data are created based on only the simple synthetic dataset, while the incident EM fields are produced by the sources surrounding ROI. Compared with traditional approaches, EMF modeling has been realized with high accuracy and the greatly reduced computation time. Unlike most of reported DL-based methods, the proposed method can work on the extremely-broad frequency band. Numerical examples based on class-specific 2-D dielectric objects demonstrate the validity of this broad-band EMF solver, which acts as the potential candidate for modeling EMF process in real time.