Transient Heterogeneous Electromagnetic Simulation With DGTD and Behavioral Macromodel

Zhang, Huan‐Huan; Jiang, Li Jun; Yao, He Ming; Zhang, Yu

doi:10.1109/temc.2016.2642955

Cited by 42 publications

(7 citation statements)

References 27 publications

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“…methods have been coupled with SBF macromodel for this kind of problem [23,[41][42][43][44][45]. An example of this case is presented in Fig.…”

Section: Electromagnetic-circuital Co-simulation Methodsmentioning

confidence: 99%

“…6(a) shows the pseudocodes of electromagnetic-circuit co-simulation. A detailed implementation of the electromagnetic-circuit coupling can be found [37,41].…”

Section: ) Electromagnetic-circuit Couplingmentioning

confidence: 99%

“…Electromagnetic-circuital co-simulation of digital circuits: (a) the ADS schematic diagram for a microtrip structure loaded with digital circuits, (b) port voltage, (c) port current. Reprinted with permission from[41].…”

mentioning

confidence: 99%

See 2 more Smart Citations

Electromagnetic-Circuital-Thermal Multiphysics Simulation Method: A Review (Invited)

Zhang¹,

Wang²,

Zhang³

et al. 2020

PIER

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“…methods have been coupled with SBF macromodel for this kind of problem [23,[41][42][43][44][45]. An example of this case is presented in Fig.…”

Section: Electromagnetic-circuital Co-simulation Methodsmentioning

confidence: 99%

“…6(a) shows the pseudocodes of electromagnetic-circuit co-simulation. A detailed implementation of the electromagnetic-circuit coupling can be found [37,41].…”

Section: ) Electromagnetic-circuit Couplingmentioning

confidence: 99%

See 1 more Smart Citation

Electromagnetic-Circuital-Thermal Multiphysics Simulation Method: A Review (Invited)

Zhang¹,

Wang²,

Zhang³

et al. 2020

PIER

Self Cite

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“…The application of machine learning (ML) in advanced computational electromagnetics, such as object monitoring [16], electromagnetic simulation [17][18][19][20][21] and field-circuit cosimulations [22,23], was initiated a long time ago. Machine learning methods aim to derive the potential mapping disciplinarian by employing the same pattern's training data and predicting the new output.…”

Section: Introductionmentioning

confidence: 99%

Snow Parameters Inversion from Passive Microwave Remote Sensing Measurements by Deep Convolutional Neural Networks

Yao

Zhang

Jiang

et al. 2022

Sensors

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This paper proposes a novel inverse method based on the deep convolutional neural network (ConvNet) to extract snow’s layer thickness and temperature via passive microwave remote sensing (PMRS). The proposed ConvNet is trained using simulated data obtained through conventional computational electromagnetic methods. Compared with the traditional inverse method, the trained ConvNet can predict the result with higher accuracy. Besides, the proposed method has a strong tolerance for noise. The proposed ConvNet composes three pairs of convolutional and activation layers with one additional fully connected layer to realize regression, i.e., the inversion of snow parameters. The feasibility of the proposed method in learning the inversion of snow parameters is validated by numerical examples. The inversion results indicate that the correlation coefficient (R2) ratio between the proposed ConvNet and conventional methods reaches 4.8, while the ratio for the root mean square error (RMSE) is only 0.18. Hence, the proposed method experiments with a novel path to improve the inversion of passive microwave remote sensing through deep learning approaches.

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“…problems [14]- [17] and field-circuit cosimulations [18], [19]. The computing process within PML can be concluded as repeatively obtaining local field by using local and neighbouring field information in the previous and current step.…”

Section: Introductionmentioning

confidence: 99%

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.

show abstract

Transient Heterogeneous Electromagnetic Simulation With DGTD and Behavioral Macromodel

Cited by 42 publications

References 27 publications

Electromagnetic-Circuital-Thermal Multiphysics Simulation Method: A Review (Invited)

Electromagnetic-Circuital-Thermal Multiphysics Simulation Method: A Review (Invited)

Snow Parameters Inversion from Passive Microwave Remote Sensing Measurements by Deep Convolutional Neural Networks

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

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