AH FDTD Method and Its Application in Solving Electromagnetic Forward and Inverse Problems Based on Machine Learning

In this letter, we explore the feasibility of using machine learning (ML) to address time-domain electromagnetic forward and inverse problems. A novel ML-associated Hermite (AH) finite-difference time-domain (FDTD) solution method is proposed, which utilizes datasets generated by the AH FDTD method to train the neural network model, and then uses this trained model for prediction. Both the prediction of the target scattering field and the inversion of the target can achieve good results. Moreover, in the task of predicting the locations of metal tunnels, the frequency-domain convolutional neural network performs better than Galerkin Transformer, and simulation of this case using AH FDTD method, the required dataset can be obtained in about 2 h, whereas FDTD takes nearly 41 h.