Abstract
In this letter, a novel absorbing boundary condition (ABC) computation method for finite-difference time-domain (FDTD) is proposed based on the machine learning approach. The hyperbolic tangent basis function (HTBF) neural network is introduced to replace traditional perfectly matched layer (PML) ABC during the FDTD solving process. The field data on the interface of conventional PML are employed to train HTBF-based PML model. Compared to the conventional approach, the novel method greatly decreases the size of a computation domain and the computation complexity of FDTD because the new model only involves the one-cell boundary layer. Numerical examples are provided to benchmark the performance of the proposed method. The results demonstrate that the newly proposed method could replace conventional PML and could be integrated into FDTD solving process with satisfactory accuracy and compatibility to FDTD. According to our knowledge, this proposed model combined artificial neural network (ANN) model is an unreported new approach based on a machine learning based for FDTD.
Recommended Citation
H. M. Yao and L. Jiang, "Machine-Learning-Based PML For The FDTD Method," IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 1, pp. 192 - 196, article no. 8567933, Institute of Electrical and Electronics Engineers, Jan 2019.
The definitive version is available at https://doi.org/10.1109/LAWP.2018.2885570
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
Finite-difference time-domain (FDTD); hyperbolic tangent basis function (HTBF) neural network; machine learning; perfectly matched layer (PML)
International Standard Serial Number (ISSN)
1536-1225
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2019
Comments
National Natural Science Foundation of China, Grant FA2386-17-1-0010