Deep Long Short-Term Memory Networks-Based Solving Method for the FDTD Method: 2-D Case
Huan Huan Zhang, He Ming Yao, Lijun Jiang, Michael Ng
Abstract
In this letter, a novel finite difference time domain (FDTD) solving method is proposed based on the deep long short-term memory (LSTM) networks. The field data in the object domain of traditional FDTD method are applied to train the newly proposed LSTM-based FDTD model, termed as LSTM-FDTD. Distinguished from the traditional FDTD method, the proposed method is not limited by Courant–Friedrichs–Levy (CFL) condition and does not need the conventional absorbing boundary conditions (ABCs). Thus, the proposed method conveniently decreases both the size of computation domain and the algorithm’s complexity. In addition, LSTM-FDTD could reach higher accuracy due to the sequence dependence of LSTM networks. Numerical benchmarks illustrate the efficiency and accuracy of the proposed method.