An Efficient Time-Domain Electromagnetic Algorithm Based on LSTM Neural Network

Abstract

Although neural networks have been applied in many fields since they were first introduced, the feasibility of applying it to predict the solution of Maxwell's equations remains open. In this letter, we investigate the feasibility of utilizing the long- and short-term memory (LSTM) neural network to solve the time-domain electromagnetic (TDEM) forward problems. With ground truth datasets being generated from the finite-difference time-domain (FDTD) method, a novel LSTM-TDEM model structure is proposed, and trained by a new feasible algorithm specifically designed for time-domain simulation. The strong approximation ability of the LSTM-TDEM method can accurately predict the electromagnetic field distributions for topologies of different geometries, materials, and excitations at different locations. The effectiveness of the proposed LSTM-TDEM method has been validated by several numerical experiments. The average relative error can be as small as 0.63% for 2-D case and 0.35% for 3-D case. It is worth noting that the training data are only 5% (198/4087) and 30% (660/2189) in 2-D and 3-D cases, respectively. Meanwhile, compared with the traditional FDTD method, the proposed method greatly reduces the calculation time, with its speedup ratio more than 1800 and 44 000 times over the FDTD method in 2-D and 3-D cases, respectively.