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.
Highlights
Absorbing boundary conditions (ABCs) are widely utilized to truncate the computational area for unbounded problems during Finite-Difference Time-Domain (FDTD) solving process [1]–[3]
We propose a new Perfectly matched layer (PML) model based on the Long Short-Term Memory (LSTM) network
Jiang: Enhanced PML Based on the LSTM Network for the FDTD Method are: (1) The LSTM based PML model reduces the computation domain and thereby the computation complexity for the FDTD ABC, because this new model only needs one-layer cell; (2) The newly proposed LSTM based PML is very convenient and flexible to be implemented in various FDTD application scenarios; (3) The accuracy of this proposed LSTM based PML model has been greatly increased, compared to the previous artificial neural network (ANN) based PML [20]
Summary
Absorbing boundary conditions (ABCs) are widely utilized to truncate the computational area for unbounded problems during FDTD solving process [1]–[3]. The electromagnetic (EM) field data on the interface between the first layer of conventional PML and the object domain are utilized to train the deep LSTM network model. The trained LSTM based PML model can replace conventional PML in multi-layer to reduce the computation complexity for FDTD solving process. Are: (1) The LSTM based PML model reduces the computation domain and thereby the computation complexity for the FDTD ABC, because this new model only needs one-layer cell; (2) The newly proposed LSTM based PML is very convenient and flexible to be implemented in various FDTD application scenarios; (3) The accuracy of this proposed LSTM based PML model has been greatly increased (about 5dB), compared to the previous ANN based PML [20]. )p with p chosen as 1 or 2, while σmax acts as the conductivity at the outermost layer of PML
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