Abstract
In this paper, two novel computational processes are proposed to solve Finite-Difference Time-Domain (FDTD) based on machine learning deep neural networks. The field and boundary conditions are employed to establish recurrent neural network FDTD (RNN-FDTD) model and convolution neural network FDTD (CNN-FDTD) model respectively. Numerical examples from scalar wave equations are provided to benchmark the performance of the proposed methods. The results demonstrate that the newly proposed methods could solve FDTD steps with satisfactory accuracy. According to our knowledge, these are unreported new approaches for machine learning based FDTD solving methods.
Recommended Citation
H. M. Yao and L. J. Jiang, "Machine Learning Based Neural Network Solving Methods For The FDTD Method," 2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, APSURSI 2018 - Proceedings, pp. 2321 - 2322, article no. 8608745, Institute of Electrical and Electronics Engineers, Jan 2018.
The definitive version is available at https://doi.org/10.1109/APUSNCURSINRSM.2018.8608745
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
CNN; FDTD; Machine Learning; RNN
International Standard Book Number (ISBN)
978-153867102-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2018
Comments
National Natural Science Foundation of China, Grant GRF 17207114