Implementing the Fast Full-Wave Electromagnetic Forward Solver Using the Deep Convolutional Encoder-Decoder Architecture

In this communication, a novel deep learning (DL)-based solver is proposed for the electromagnetic forward (EMF) process. It is based on the complex-valued deep convolutional neural networks (DConvNets) comprising an encoder network and a corresponding decoder network with pixel-wise regression layer. The encoder network takes the incident EM wave and the contrast (permittivity) distribution of the object as the input. It channels the processed data into the corresponding decoder network to predict the total EM field due to the scatter of the input incident EM wave. The training of the proposed DConvNets is done using the simple synthetic dataset. Due to its strong approximation capability, the proposed DConvNets can realize the prediction of EM field. Hence, the proposed DL-based EMF solver acts as a inhomogeneous transformation-the unknown EM field in the objective domain is obtained through the transformation from the information of the incident EM field and the distribution of contrasts (permittivities). Compared with conventional methods, the EMF problem can be solved with higher accuracy and significantly reduced CPU time. Numerical examples have demonstrated the feasibility of this newly proposed approach. This newly proposed DL-based EMF solver presents a new alternative to electromagnetic computation approaches.

Automated summary

This communication proposes a novel deep learning-based solver for the electromagnetic forward (EMF) process. The proposed solver uses complex-valued deep convolutional neural networks to predict the total EM field. By training the networks with a simple synthetic dataset, the solver achieves high accuracy and significantly reduces CPU time for solving the EMF problem.