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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
