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.


Electrical and Computer Engineering


National Natural Science Foundation of China, Grant FA2386-17-1-0010

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)


Document Type

Article - Journal

Document Version


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Publication Date

01 Jan 2019