Abstract
This paper proposes a new absorbing boundary condition (ABC) computation approach based on the deep learning technique. Benefited from the sequence dependence feature, the Long Short-Term Memory (LSTM) network is employed to replace the conventional perfectly matched layer (PML) ABC for the Finite-Difference Time-Domain (FDTD) solving process. The newly proposed LSTM based PML model is trained by the electromagnetic field data on the interface of the conventional PML. Different from the conventional PML, the newly proposed model only needs one cell layer as the boundary. Hence, the newly proposed method conveniently reduces both the algorithm's complexity and the area of computation domain of FDTD. Additionally, the newly proposed LSTM based PML model can achieve higher accuracy than the conventional artificial neural network (ANN) based PML, thanks to the sequence dependence feature of the LSTM networks. Numerical examples have illustrated the capability and the accuracy of the proposed LSTM model. The results illustrate that the new method can be compatibly embedded into the FDTD solving process with the high accuracy.
Recommended Citation
H. M. Yao and L. Jiang, "Enhanced PML Based On The Long Short Term Memory Network For The FDTD Method," IEEE Access, vol. 8, pp. 21028 - 21035, article no. 8970334, Institute of Electrical and Electronics Engineers, Jan 2020.
The definitive version is available at https://doi.org/10.1109/ACCESS.2020.2969569
Department(s)
Electrical and Computer Engineering
Publication Status
Open Access
Keywords and Phrases
deep learning; FDTD; LSTM network; PML
International Standard Serial Number (ISSN)
2169-3536
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 The Authors, All rights reserved.
Publication Date
01 Jan 2020
Comments
National Natural Science Foundation of China, Grant FA2386-17-1-0010