Design of SIW Filters in D-band Using Invertible Neural Nets

Yu, Huan; Torun, Hakki Mert; Rehman, Mutee ur; Swaminathan, Madhavan

doi:10.1109/ims30576.2020.9223952

Cited by 15 publications

(3 citation statements)

References 14 publications

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“…ANNs have been employed as useful tools for inverse modeling of microwave components [73], [74], [75], [76], [77], [78], [148], [170]. In recent years, various advanced neural network-based inverse modeling techniques have been developed for microwave CAD to address the challenging nonuniqueness problem for inverse modeling [79], [80], [81], [82], [83], [171].…”

Section: Neural Network-based Inverse Modelingmentioning

confidence: 99%

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Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

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This article presents an overview of artificial neural network (ANN) techniques for a microwave computer-aided design (CAD). ANN-based techniques are becoming useful for performing forward/inverse modeling for active/passive components to enhance a circuit design. With measured or simulated data of microwave devices, ANNs can be trained to learn relevant microwave relationships, which are, otherwise, computationally expensive or for which efficient analytical formulas are not available. Fundamental concepts of the ANN structure and training, such as feedforward neural networks (FFNNs), recurrent neural networks (RNNs)/dynamic neural networks (DNNs)/time-delay neural networks (TDNNs), deep neural networks, and neural network training and extrapolation, are described. Knowledgebased neural networks (KBNNs) are described for improving the accuracy and reliability of modeling and design optimization. Various advanced ANN techniques, such as neuro-transfer function (neuro-TF) modeling, neural network inverse modeling, and deep neural network modeling, are discussed. The existing and emerging applications of ANN in microwave CAD are identified, such as electromagnetic (EM)/multiphysics modeling, modeling of nonlinear circuits and transistors, filter design, very largescale integration (VLSI) interconnects, oscillator, transmitter and receiver modeling, and CAD applications in such as gallium nitride (GaN) high electron-mobility transistor (HEMT), wireless power transfer (WPT), microelectromechanical system (MEMS), and substrate-integrated waveguide (SIW).

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Section: Neural Network-based Inverse Modelingmentioning

confidence: 99%

“…Invertible neural network models are also developed to handle the inverse modeling problem [171]. Invertible neural networks consist of reversible blocks.…”

Section: E Invertible Neural Nets For Inverse Modelingmentioning

confidence: 99%

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

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“…When plenty of simulated data is readily available, a highcomplexity model can be trained, such that the optimal parameter combination is accurately predicted. In this case, optimization strategies can be built upon powerful machine learning (ML) models such as artificial neural network (ANN), which have been widely employed for modeling and optimization of microwave devices [2], [3]. Less computationally intensive surrogates include Gaussian process (GP) regression [4], [5] and Support Vector Machines [6].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Optimization for Microwave Devices Using Deep GP Spectral Surrogate Models

Garbuglia

Spina

Deschrijver

et al. 2023

IEEE Trans. Microwave Theory Techn.

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In microwave design, Bayesian optimization (BO) techniques have been widely applied to the optimization of the frequency response of components and devices. The common approach in BO is to model and maximize an objective function over the design parameters, in order to find the optimal spectral response. Such an approach avoids the direct modeling of spectral responses, which is a challenging task for the typical data-efficient surrogate models used in BO. Simple objective functions may lead to a suboptimal solutions, while complicated objectives require more powerful and less data-efficient surrogate models. To resolve this issue, this article proposes to adopt a deep Gaussian process (DGP) to directly model all relevant S coefficients over the frequency and the design parameter ranges of interest. Subsequently, an objective probability distribution is retrieved from the DGP model and maximized using a BO scheme. The proposed approach is tested on two suitable microwave examples and compared to the standard BO approach. Results show increased accuracy in identifying the optimal frequency response for the given design parameters and the desired objective, while maintaining high data efficiency.

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Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction

Zou,

Gong,

Zeng

et al. 2024

J Digit Imaging. Inform. med.

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Design of SIW Filters in D-band Using Invertible Neural Nets

Cited by 15 publications

References 14 publications

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Bayesian Optimization for Microwave Devices Using Deep GP Spectral Surrogate Models

Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction

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