Machine-Learning-Based PML for the FDTD Method

Yao, He Ming; Jiang, Lijun

doi:10.1109/lawp.2018.2885570

Cited by 72 publications

(22 citation statements)

References 22 publications

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“…The performance of our LSTM model in the online application stage is demonstrated in Fig. 6, where the relative errors of LSTM PML model at Point A and B are compared both with the 5-cell and 1-cell conventional PML and with the previously proposed HTBF based PML [20]. As shown in Fig.…”

Section: Model Embeddingmentioning

confidence: 96%

“…3) NUMBER OF HIDDEN UNIT in most cases, the determination of the exact number of hidden unit for h t is still an important research area for deep learning [18]- [20], [39]- [40]. In this paper, as the most commonly used strategy for deep learning, the trialand-error process is taken to ensure the number of hidden unit [18]- [20]. While various number of hidden units can be tried, the expected values are the smaller ones resulting into smaller training errors and less computation.…”

Section: ) Activation Functionmentioning

confidence: 99%

“…We use several representative numerical benchmarks to validate the performance of this newly proposed LSTM based PML model. The conventional 1-cell PML, the conventional 5-cell PML and the HTBF based PML [20] are utilized to compare with the proposed LSTM based PML.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…This feature makes ML approaches adaptable to substitute for the computation process of conventional PML. The previous work has demonstrated the application of artificial neural network (ANN) model into replacing conventional PML [20]. But, the inevitably accumulative error in artificial neural network computation process greatly limits the usage of ANN based PML for FDTD solving process.…”

Section: Introductionmentioning

confidence: 99%

“…The trained LSTM based PML model can replace conventional PML in multi-layer to reduce the computation complexity for FDTD solving process. The advantages of the newly proposed 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 (about 5dB), compared to the previous ANN based PML [20].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced PML Based on the Long Short Term Memory Network for the FDTD Method

Yao

Jiang

2020

IEEE Access

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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.

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Section: Model Embeddingmentioning

confidence: 96%