Multi-Physics Parametric Modeling of Microwave Passive Components Using Artificial Neural Networks

Yan, Shuxia; Zhang, Yaoqian; Jin, Xiaoyi; Zhang, Wei; Shi, Weiguang

doi:10.2528/pierm18070403

Cited by 9 publications

(18 citation statements)

References 15 publications

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“…In [26], a correlating mapping is developed so that the multiphysics non-geometrical design variables can be mapped to the geometrical design variables for the simple EM structure. A multiphysics parametric modeling method is introduced using artificial neural networks in [27]. In [28], a space mapping approach is used to build the links between the multiphysics domain and the pure EM domain in an effort to reduce the multiphysics model development time.…”

Section: Introductionmentioning

confidence: 99%

Multiphysics Parametric Modeling of Microwave Components Using Combined Neural Networks and Transfer Function

et al. 2020

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This paper proposes a new technique to develop an accurate multiphysics parametric model for microwave components to speed up the multiphysics modeling process. In the proposed technique, the artificial neural networks (ANNs) and pole/residue based transfer function are incorporated to represent the highly non-linear relationships between electromagnetic centric (EM-centric) multiphysics behaviors and multiphysics geometrical/non-geometrical design parameters. Vector fitting technique is utilized to obtain the poles/residues of the transfer function for each multiphysics sample. Since the relationship between multiphysics design parameters and the pole/residues of the transfer function is non-linear and unknown, two mapping functions are proposed to establish the mathematical links between the multiphysics design parameters and poles/residues. Parallel multiphysics data generation is proposed to generate the training and testing data for establishing the proposed multiphysics parametric model. A two stage training algorithm is proposed to guide the multiphysics training process. Once an accurate overall model is developed, it can be used to provide accurate and fast prediction of the multiphysics behavior of microwave components with geometrical and non-geometrical parameters as variables, and further can be used in the high level design. Compared with the existing multiphysics modeling methods, the proposed technique can achieve better model accuracy with high efficiency. The proposed technique provides an accurate and efficient methodology even when the coarse model or empirical model is unavailable. Two microwave examples are used to illustrate the validity of the proposed multiphysics parametric modeling technique. INDEX TERMS Artificial neural networks, multiphysics, parametric modeling, parallel computation, transfer function.

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Section: Introductionmentioning

confidence: 99%

Multiphysics Parametric Modeling of Microwave Components Using Combined Neural Networks and Transfer Function

et al. 2020

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“…The trained ANNs can be used for high-level microwave system design to provide fast and accurate solutions to the task they have learned. In [18], neural network modeling is introduced to multiphysics parametric modeling area. ANNs are trained to learn the nonlinear relationship between EM behaviors and multiphysics design variables, then provide effective and fast prediction of EM responses with respect to the multiphysics design parameters.…”

Section: Introductionmentioning

confidence: 99%

“…ANNs are trained to learn the nonlinear relationship between EM behaviors and multiphysics design variables, then provide effective and fast prediction of EM responses with respect to the multiphysics design parameters. The neural-based multiphysics parametric modeling in [18] is a step-by-step manual process, which involves sequential multiphysics data generation, neural network selection, training and testing. This multiphysics parametric modeling process is manually carried out and requires intensive human effort and experience.…”

Section: Introductionmentioning

confidence: 99%

Automated Neural Network-Based Multiphysics Parametric Modeling of Microwave Components

Zhang

Yan

et al. 2019

IEEE Access

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This paper proposes a novel technique for automated neural network based multiphysics parametric modeling of microwave components. For the first time, we propose to utilize automated model generation (AMG) algorithm in the field of electromagnetic (EM) centric multiphysics parametric model development to improve the neural-based multiphysics modeling efficiency. All the subtasks in developing a neural network based multiphysics parametric model, including EM centric multiphysics data generation, neural network structure adaptation, training and testing, are integrated into one unified and automated framework, thus converting the conventional human-based manual modeling into an automated computational process. In the proposed algorithm, automated EM centric multiphysics data generation is realized by automatic driving of multiphysics simulation tools. Parallel computation technique is incorporated to further speedup the data generation process by driving multiple EM centric multiphysics simulations on parallel computers simultaneously. In addition, automated neural model structure adaptation algorithm for multiphysics parametric modeling is also proposed. In this way, the proposed technique automates the neuralbased multiphysics model development process and significantly reduces the intensive human effort and modeling time demanded by the conventional manual multiphysics modeling methods. The achieved neural model can be used to provide accurate and fast prediction of the EM centric multiphysics responses of microwave components in high-level multiphysics design. Examples of multiphysics parametric modeling of two microwave filters are presented to show the advantage of this work.INDEX TERMS Design automation, multiphysics modeling, neural networks, parallel computation, parametric modeling.

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“…Recent research efforts of ANN-based EM parametric modeling techniques have focused on automated model generation (AMG) methods [13], [51], [65], hybrid training methods incorporating parallel processing [34], and multiphysics parametric modeling [66], [67]. In [51], an advanced algorithm for AMG using neural networks has been presented, where interpolation techniques are incorporated to avoid redundant training in AMG, accelerating the overall model generation process.…”

Section: Thesis Organizationmentioning

confidence: 99%

“…More recently, ANNs have been introduced to multiphysics parametric modeling of microwave components. In [66], neural networks have been applied to multiphysics parametric modeling of microwave filters.…”

Section: Thesis Organizationmentioning

confidence: 99%

Advanced Parametric Modeling and Yield Optimization Approaches for Microwave Passive Components

Zhang¹

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Parametric modeling and yield optimization techniques play important roles in electromagnetic (EM)-based microwave component design. In the first part of this thesis, we propose a novel training approach for parametric modeling of microwave passive components with respect to changes in geometrical parameters using matrix Padé via Lanczos (MPVL) and EM sensitivities. In the proposed approach, the EM responses of passive components versus frequency are represented by polezero-gain transfer functions. The relationships between the poles/zeros/gain in the transfer function and geometrical variables are learnt by neural networks. A novel

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Multi-Physics Parametric Modeling of Microwave Passive Components Using Artificial Neural Networks

Cited by 9 publications

References 15 publications

Multiphysics Parametric Modeling of Microwave Components Using Combined Neural Networks and Transfer Function

Multiphysics Parametric Modeling of Microwave Components Using Combined Neural Networks and Transfer Function

Automated Neural Network-Based Multiphysics Parametric Modeling of Microwave Components

Advanced Parametric Modeling and Yield Optimization Approaches for Microwave Passive Components

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