Fast Multi-Physics Simulation of Microwave Filters via Deep Hybrid Neural Network

Zhou, Yuanguo; Xie, Jianan; Ren, Qiang; Zhang, Huan Huan; Liu, Qing

doi:10.1109/tap.2022.3188627

Cited by 11 publications

(3 citation statements)

References 38 publications

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“…The data groups are divided into a training set of 5000 data groups and a test set of 1000 data groups. Instead of randomly accumulating data sets by parameter sweeping, the design‐of‐experiment (DOE) method 37 is used in the process of data collection, which means all data with labels will provide useful features for training DNN.…”

Section: Neural Network For Obtaining the Pul Parametersmentioning

confidence: 99%

Crosstalk analysis of twisted bundle of twisted wire pairs (TBTWPs) and star‐quad cables under the effect of inner and outer excitations

Su,

Gassab,

Zhang

et al. 2023

Int J Numerical Modelling

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In this article, deep neural network (DNN) and chain parameter methods are used to analyze the crosstalk coupling of a star‐quad and a twisted bundle of twisted wire pairs (TBTWP) cables for different excitations, respectively. A DNN is used to extract the per‐unit‐length (p.u.l) resistance, inductance, capacitance, and conductance (RLCG) parameters at any position for the star‐quad and TBTWP cables. The induced common mode (CM) and differential mode (DM) currents due to crosstalk are calculated by combining the obtained RLCG parameters with the chain parameter method. The obtained results show good agreement with that of FEKO software. Moreover, it is found that the star‐quad cable can reduce the external coupling more effectively than that of the TBTWP cable. Finally, measurements are performed to further validate the accuracy of our proposed algorithm.

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Section: Neural Network For Obtaining the Pul Parametersmentioning

confidence: 99%

Crosstalk analysis of twisted bundle of twisted wire pairs (TBTWPs) and star‐quad cables under the effect of inner and outer excitations

Su,

Gassab,

Zhang

et al. 2023

Int J Numerical Modelling

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show abstract

“…Deep learning (DL) enhanced the ability of ANNs by utilizing deeper networks and more neurons and has become a dominant paradigm in addressing more complex problems [21], [22]. In [23], a hybrid DNN model was proposed to model and simulate microwave filters, leading to a higher accurate result with fewer samples than traditional ANNs. To further improve training efficiency, domain knowledge was employed to assist DNNs with the design of metalens antenna [24], metasurfaces [25][26][27][28], frequency selective surface (FSS) [29], [30], mode recognition [31], and reflectarray [32].…”

mentioning

confidence: 99%

Fully Automated Design Method Based on Reinforcement Learning and Surrogate Modeling for Antenna Array Decoupling

Wei

Zhou

Wang

et al. 2023

IEEE Trans. Antennas Propagat.

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Modern electromagnetic (EM) device design generally relies on extensive iterative optimizations by designers using simulation software (e.g. CST), which is a very timeconsuming and tedious process. To relieve human engineers and boost productivity, we proposed a machine learning framework to solve the problem of automated design for EM tasks. The proposed approach combines advanced reinforcement learning (RL) algorithms and deep neural networks (DNNs) in an attempt to simulate the decision-making process of human designers to realize automation learning. Specifically, the RL-based agent can interact with the EM design software without engaging human designers, allowing for automated design. Besides, the data accumulated during EM software simulation in the early design stage are reused as training data to build a DNN surrogate model to replace the time-consuming EM simulation and further accelerate the training of RL to achieve better optimization of EM design. Two types of antenna array decoupling including 1×2 and 1×4 arrays working at 3.5 GHz are used as test vehicles to validate the proposed method. The decoupling metasurfaces designed by the proposed fully automated method based on RL showed satisfactory results comparable to the results achievable by human designers. This indicates that the proposed method can be used to build powerful tools to boost the design efficiency of EM devices.

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“…For multi-physics modeling of microwave filters, the authors used a deep hybrid neural network [10]. The network was conceived as a cascade of ANNs with different parameters.…”

mentioning

confidence: 99%

Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks

et al. 2023

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In this communication, artificial neural networks are used to estimate the initial structure of a multiband planar antenna. The neural networks are trained on a set of selected normalized multiband antennas characterized by time-efficient modal analysis with limited accuracy. Using the Deep Learning Toolbox in Matlab, several types of neural networks have been created and trained on the sample planar multiband antennas. In the neural network learning process, suitable network types were selected for the design of these antennas. The trained networks, depending on the desired operating bands, will select the appropriate antenna geometry. This is further optimized using Newton’s method in HFSS. The use of the neural pre-design concept speeds up and simplifies the design of multiband planar antennas. The findings presented in this paper will be used to refine and accelerate the design of planar multiband antennas.

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Fast Multi-Physics Simulation of Microwave Filters via Deep Hybrid Neural Network

Cited by 11 publications

References 38 publications

Crosstalk analysis of twisted bundle of twisted wire pairs (TBTWPs) and star‐quad cables under the effect of inner and outer excitations

Crosstalk analysis of twisted bundle of twisted wire pairs (TBTWPs) and star‐quad cables under the effect of inner and outer excitations

Fully Automated Design Method Based on Reinforcement Learning and Surrogate Modeling for Antenna Array Decoupling

Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks

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