Journal
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS
Volume 18, Issue 1, Pages 192-196Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LAWP.2018.2885570
Keywords
Finite-difference time-domain (FDTD); hyperbolic tangent basis function (HTBF) neural network; machine learning; perfectly matched layer (PML)
Funding
- Research Grants Council of Hong Kong [GRF 17207114, GRF 17210815]
- Asian Office of Aerospace Research and Development (AOARD) [FA2386-17-1-0010]
- National Natural Science Foundation (NSFC) [61271158]
- Hong Kong University Grants Committee (UGC) [AoE/P-04/08]
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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.
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