The Experts below are selected from a list of 61716 Experts worldwide ranked by ideXlab platform

Qijun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • space mapping approach to electromagnetic centric multiphysics Parametric Modeling of microwave components
    IEEE Transactions on Microwave Theory and Techniques, 2018
    Co-Authors: Wei Zhang, Feng Feng, Venumadhavreddy Gongalreddy, Jianan Zhang, Qijun Zhang
    Abstract:

    This paper proposes a novel technique to develop a low-cost electromagnetic (EM) centric multiphysics Parametric model for microwave components. In the proposed method, we use space mapping techniques to combine the computational efficiency of EM single physics (EM only) simulation with the accuracy of the multiphysics simulation. The EM responses with respect to different values of geometrical parameters in nondeformed structures without considering other physics domains are regarded as coarse model. The coarse model is developed using the Parametric Modeling methods such as artificial neural networks or neuro-transfer function techniques. The EM responses with geometrical and nongeometrical design parameters as variables in the practical deformed structures due to thermal and structural mechanical stress factors are regarded as fine model. The fine model represents the behavior of EM centric multiphysics responses. The proposed model includes the EM domain coarse model and two mapping neural networks to map the EM domain (single physics) to the multiphysics domain. Our proposed technique can achieve good accuracy for multiphysics Parametric Modeling with fewer multiphysics training data and less computational cost. After the Modeling process, the proposed model can be used to provide accurate and fast prediction of EM centric multiphysics responses of microwave components with respect to the changes of design parameters within the training ranges. The proposed technique is illustrated by a tunable four-pole waveguide filter example at 10.5–11.5 GHz and an iris coupled microwave cavity filter example at 690–720 MHz.

  • Parametric Modeling of microwave components using adjoint neural networks and pole residue transfer functions with em sensitivity analysis
    IEEE Transactions on Microwave Theory and Techniques, 2017
    Co-Authors: Feng Feng, Venumadhavreddy Gongalreddy, Chao Zhang, Qijun Zhang
    Abstract:

    This paper proposes a pole-residue-based adjoint neuro-transfer function (neuro-TF) technique with electromagnetic (EM) sensitivity analysis for Parametric Modeling of EM behavior of microwave components with respect to changes in geometrical parameters. The purpose is to increase model accuracy by utilizing EM sensitivity information and to speed up model development by reducing the number of training data required for developing the model. The proposed Parametric model consists of original and adjoint pole-residue based neuro-TF models. New formulations are derived for calculating the second-order derivatives for training the adjoint pole-residue-based neuro-TF model. An advanced pole-residue tracking technique is proposed to exploit the sensitivity information to track the splitting of poles as geometrical parameters change. This pole-residue tracking technique allows the model to bridge the differences of the orders of transfer function over different regions of the geometrical parameters, and ultimately form smooth and continuous functions between the pole/residues and the geometrical variables. The proposed technique addresses the challenges of tracking pole splitting when training data are limited. By exploiting the sensitivity information, the proposed technique can speed up the model development process over the existing pole-residue Parametric Modeling method which does not use sensitivity analysis.

  • advanced Parametric Modeling using neuro transfer function for em based multiphysics analysis of microwave passive components
    International Microwave Symposium, 2016
    Co-Authors: Wei Zhang, Feng Feng, Venumadhavreddy Gongalreddy, Jianan Zhang, Shunlu Zhang, Qijun Zhang
    Abstract:

    This paper proposes a novel Parametric Modeling technique for electromagnetic (EM) based multiphysics analysis of microwave passive components. Multiphysics parameters usually affect the EM performance by indirectly influencing the geometrical variables, such as thermal effect causing an expansion in the geometrical parameters, and stress inducing the physical deformation. In the proposed technique, the input classification and correlating mapping is introduced to transform the multiphysics input parameters into geometrical input parameters. Further, the combined neural network and transfer function technique (neuro-TF) is used to model the EM responses w.r.t. the transformed geometrical variables. The model obtained using the proposed technique can achieve good accuracy with low complexity of neural networks, and further can be used in the high-level design. One tunable evanescent mode cavity filter example is used to demonstrate the validity of this technique.

  • Parametric Modeling of em behavior of microwave components using combined neural networks and pole residue based transfer functions
    IEEE Transactions on Microwave Theory and Techniques, 2016
    Co-Authors: Feng Feng, Chao Zhang, Qijun Zhang
    Abstract:

    This paper proposes an advanced technique to develop combined neural network and pole-residue-based transfer function models for Parametric Modeling of electromagnetic (EM) behavior of microwave components. In this technique, neural networks are trained to learn the relationship between pole/residues of the transfer functions and geometrical parameters. The order of the pole-residue transfer function may vary over different regions of geometrical parameters. We develop a pole-residue tracking technique to solve this order-changing problem. After the proposed Modeling process, the trained model can be used to provide accurate and fast prediction of the EM behavior of microwave components with geometrical parameters as variables. The proposed method can obtain better accuracy in challenging applications involving high dimension of geometrical parameter space and large geometrical variations, compared with conventional Modeling methods. The proposed technique is effective and robust especially in solving high-order problems. This technique is illustrated by three examples of EM Parametric Modeling.

  • Parametric Modeling of microwave passive components using sensitivity analysis based adjoint neural network technique
    IEEE Transactions on Microwave Theory and Techniques, 2013
    Co-Authors: Sayed Alireza Sadrossadat, Qijun Zhang
    Abstract:

    This paper presents a novel sensitivity-analysis-based adjoint neural-network (SAANN) technique to develop Parametric models of microwave passive components. This technique allows robust Parametric model development by learning not only the input–output behavior of the Modeling problem, but also derivatives obtained from electromagnetic (EM) sensitivity analysis. A novel derivation is introduced to allow complicated high-order derivatives to be computed by a simple artificial neural-network (ANN) forward-back propagation procedure. New formulations are deduced for exact second-order sensitivity analysis of general multilayer neural-network structures with any numbers of layers and hidden neurons. Compared to our previous work on adjoint neural networks, the proposed SAANN is easier to implement into an existing ANN structure. The proposed technique allows us to obtain accurate and Parametric models with less training data. Another benefit of this technique is that the trained model can accurately predict derivatives to geometrical or material parameters, regardless of whether or not these parameters are accommodated as sensitivity variables in EM simulators. Once trained, the SAANN models provide accurate and fast prediction of EM responses and derivatives used for high-level optimization with geometrical or material parameters as design variables. Three examples including Parametric Modeling of coupled-line filters, cavity filters, and junctions are presented to demonstrate the validity of this technique.

Feng Feng - One of the best experts on this subject based on the ideXlab platform.

  • space mapping approach to electromagnetic centric multiphysics Parametric Modeling of microwave components
    IEEE Transactions on Microwave Theory and Techniques, 2018
    Co-Authors: Wei Zhang, Feng Feng, Venumadhavreddy Gongalreddy, Jianan Zhang, Qijun Zhang
    Abstract:

    This paper proposes a novel technique to develop a low-cost electromagnetic (EM) centric multiphysics Parametric model for microwave components. In the proposed method, we use space mapping techniques to combine the computational efficiency of EM single physics (EM only) simulation with the accuracy of the multiphysics simulation. The EM responses with respect to different values of geometrical parameters in nondeformed structures without considering other physics domains are regarded as coarse model. The coarse model is developed using the Parametric Modeling methods such as artificial neural networks or neuro-transfer function techniques. The EM responses with geometrical and nongeometrical design parameters as variables in the practical deformed structures due to thermal and structural mechanical stress factors are regarded as fine model. The fine model represents the behavior of EM centric multiphysics responses. The proposed model includes the EM domain coarse model and two mapping neural networks to map the EM domain (single physics) to the multiphysics domain. Our proposed technique can achieve good accuracy for multiphysics Parametric Modeling with fewer multiphysics training data and less computational cost. After the Modeling process, the proposed model can be used to provide accurate and fast prediction of EM centric multiphysics responses of microwave components with respect to the changes of design parameters within the training ranges. The proposed technique is illustrated by a tunable four-pole waveguide filter example at 10.5–11.5 GHz and an iris coupled microwave cavity filter example at 690–720 MHz.

  • Parametric Modeling of microwave components using adjoint neural networks and pole residue transfer functions with em sensitivity analysis
    IEEE Transactions on Microwave Theory and Techniques, 2017
    Co-Authors: Feng Feng, Venumadhavreddy Gongalreddy, Chao Zhang, Qijun Zhang
    Abstract:

    This paper proposes a pole-residue-based adjoint neuro-transfer function (neuro-TF) technique with electromagnetic (EM) sensitivity analysis for Parametric Modeling of EM behavior of microwave components with respect to changes in geometrical parameters. The purpose is to increase model accuracy by utilizing EM sensitivity information and to speed up model development by reducing the number of training data required for developing the model. The proposed Parametric model consists of original and adjoint pole-residue based neuro-TF models. New formulations are derived for calculating the second-order derivatives for training the adjoint pole-residue-based neuro-TF model. An advanced pole-residue tracking technique is proposed to exploit the sensitivity information to track the splitting of poles as geometrical parameters change. This pole-residue tracking technique allows the model to bridge the differences of the orders of transfer function over different regions of the geometrical parameters, and ultimately form smooth and continuous functions between the pole/residues and the geometrical variables. The proposed technique addresses the challenges of tracking pole splitting when training data are limited. By exploiting the sensitivity information, the proposed technique can speed up the model development process over the existing pole-residue Parametric Modeling method which does not use sensitivity analysis.

  • advanced Parametric Modeling using neuro transfer function for em based multiphysics analysis of microwave passive components
    International Microwave Symposium, 2016
    Co-Authors: Wei Zhang, Feng Feng, Venumadhavreddy Gongalreddy, Jianan Zhang, Shunlu Zhang, Qijun Zhang
    Abstract:

    This paper proposes a novel Parametric Modeling technique for electromagnetic (EM) based multiphysics analysis of microwave passive components. Multiphysics parameters usually affect the EM performance by indirectly influencing the geometrical variables, such as thermal effect causing an expansion in the geometrical parameters, and stress inducing the physical deformation. In the proposed technique, the input classification and correlating mapping is introduced to transform the multiphysics input parameters into geometrical input parameters. Further, the combined neural network and transfer function technique (neuro-TF) is used to model the EM responses w.r.t. the transformed geometrical variables. The model obtained using the proposed technique can achieve good accuracy with low complexity of neural networks, and further can be used in the high-level design. One tunable evanescent mode cavity filter example is used to demonstrate the validity of this technique.

  • Parametric Modeling of em behavior of microwave components using combined neural networks and pole residue based transfer functions
    IEEE Transactions on Microwave Theory and Techniques, 2016
    Co-Authors: Feng Feng, Chao Zhang, Qijun Zhang
    Abstract:

    This paper proposes an advanced technique to develop combined neural network and pole-residue-based transfer function models for Parametric Modeling of electromagnetic (EM) behavior of microwave components. In this technique, neural networks are trained to learn the relationship between pole/residues of the transfer functions and geometrical parameters. The order of the pole-residue transfer function may vary over different regions of geometrical parameters. We develop a pole-residue tracking technique to solve this order-changing problem. After the proposed Modeling process, the trained model can be used to provide accurate and fast prediction of the EM behavior of microwave components with geometrical parameters as variables. The proposed method can obtain better accuracy in challenging applications involving high dimension of geometrical parameter space and large geometrical variations, compared with conventional Modeling methods. The proposed technique is effective and robust especially in solving high-order problems. This technique is illustrated by three examples of EM Parametric Modeling.

Chao Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Parametric Modeling of microwave components using adjoint neural networks and pole residue transfer functions with em sensitivity analysis
    IEEE Transactions on Microwave Theory and Techniques, 2017
    Co-Authors: Feng Feng, Venumadhavreddy Gongalreddy, Chao Zhang, Qijun Zhang
    Abstract:

    This paper proposes a pole-residue-based adjoint neuro-transfer function (neuro-TF) technique with electromagnetic (EM) sensitivity analysis for Parametric Modeling of EM behavior of microwave components with respect to changes in geometrical parameters. The purpose is to increase model accuracy by utilizing EM sensitivity information and to speed up model development by reducing the number of training data required for developing the model. The proposed Parametric model consists of original and adjoint pole-residue based neuro-TF models. New formulations are derived for calculating the second-order derivatives for training the adjoint pole-residue-based neuro-TF model. An advanced pole-residue tracking technique is proposed to exploit the sensitivity information to track the splitting of poles as geometrical parameters change. This pole-residue tracking technique allows the model to bridge the differences of the orders of transfer function over different regions of the geometrical parameters, and ultimately form smooth and continuous functions between the pole/residues and the geometrical variables. The proposed technique addresses the challenges of tracking pole splitting when training data are limited. By exploiting the sensitivity information, the proposed technique can speed up the model development process over the existing pole-residue Parametric Modeling method which does not use sensitivity analysis.

  • Parametric Modeling of em behavior of microwave components using combined neural networks and pole residue based transfer functions
    IEEE Transactions on Microwave Theory and Techniques, 2016
    Co-Authors: Feng Feng, Chao Zhang, Qijun Zhang
    Abstract:

    This paper proposes an advanced technique to develop combined neural network and pole-residue-based transfer function models for Parametric Modeling of electromagnetic (EM) behavior of microwave components. In this technique, neural networks are trained to learn the relationship between pole/residues of the transfer functions and geometrical parameters. The order of the pole-residue transfer function may vary over different regions of geometrical parameters. We develop a pole-residue tracking technique to solve this order-changing problem. After the proposed Modeling process, the trained model can be used to provide accurate and fast prediction of the EM behavior of microwave components with geometrical parameters as variables. The proposed method can obtain better accuracy in challenging applications involving high dimension of geometrical parameter space and large geometrical variations, compared with conventional Modeling methods. The proposed technique is effective and robust especially in solving high-order problems. This technique is illustrated by three examples of EM Parametric Modeling.

Venumadhavreddy Gongalreddy - One of the best experts on this subject based on the ideXlab platform.

  • space mapping approach to electromagnetic centric multiphysics Parametric Modeling of microwave components
    IEEE Transactions on Microwave Theory and Techniques, 2018
    Co-Authors: Wei Zhang, Feng Feng, Venumadhavreddy Gongalreddy, Jianan Zhang, Qijun Zhang
    Abstract:

    This paper proposes a novel technique to develop a low-cost electromagnetic (EM) centric multiphysics Parametric model for microwave components. In the proposed method, we use space mapping techniques to combine the computational efficiency of EM single physics (EM only) simulation with the accuracy of the multiphysics simulation. The EM responses with respect to different values of geometrical parameters in nondeformed structures without considering other physics domains are regarded as coarse model. The coarse model is developed using the Parametric Modeling methods such as artificial neural networks or neuro-transfer function techniques. The EM responses with geometrical and nongeometrical design parameters as variables in the practical deformed structures due to thermal and structural mechanical stress factors are regarded as fine model. The fine model represents the behavior of EM centric multiphysics responses. The proposed model includes the EM domain coarse model and two mapping neural networks to map the EM domain (single physics) to the multiphysics domain. Our proposed technique can achieve good accuracy for multiphysics Parametric Modeling with fewer multiphysics training data and less computational cost. After the Modeling process, the proposed model can be used to provide accurate and fast prediction of EM centric multiphysics responses of microwave components with respect to the changes of design parameters within the training ranges. The proposed technique is illustrated by a tunable four-pole waveguide filter example at 10.5–11.5 GHz and an iris coupled microwave cavity filter example at 690–720 MHz.

  • Parametric Modeling of microwave components using adjoint neural networks and pole residue transfer functions with em sensitivity analysis
    IEEE Transactions on Microwave Theory and Techniques, 2017
    Co-Authors: Feng Feng, Venumadhavreddy Gongalreddy, Chao Zhang, Qijun Zhang
    Abstract:

    This paper proposes a pole-residue-based adjoint neuro-transfer function (neuro-TF) technique with electromagnetic (EM) sensitivity analysis for Parametric Modeling of EM behavior of microwave components with respect to changes in geometrical parameters. The purpose is to increase model accuracy by utilizing EM sensitivity information and to speed up model development by reducing the number of training data required for developing the model. The proposed Parametric model consists of original and adjoint pole-residue based neuro-TF models. New formulations are derived for calculating the second-order derivatives for training the adjoint pole-residue-based neuro-TF model. An advanced pole-residue tracking technique is proposed to exploit the sensitivity information to track the splitting of poles as geometrical parameters change. This pole-residue tracking technique allows the model to bridge the differences of the orders of transfer function over different regions of the geometrical parameters, and ultimately form smooth and continuous functions between the pole/residues and the geometrical variables. The proposed technique addresses the challenges of tracking pole splitting when training data are limited. By exploiting the sensitivity information, the proposed technique can speed up the model development process over the existing pole-residue Parametric Modeling method which does not use sensitivity analysis.

  • advanced Parametric Modeling using neuro transfer function for em based multiphysics analysis of microwave passive components
    International Microwave Symposium, 2016
    Co-Authors: Wei Zhang, Feng Feng, Venumadhavreddy Gongalreddy, Jianan Zhang, Shunlu Zhang, Qijun Zhang
    Abstract:

    This paper proposes a novel Parametric Modeling technique for electromagnetic (EM) based multiphysics analysis of microwave passive components. Multiphysics parameters usually affect the EM performance by indirectly influencing the geometrical variables, such as thermal effect causing an expansion in the geometrical parameters, and stress inducing the physical deformation. In the proposed technique, the input classification and correlating mapping is introduced to transform the multiphysics input parameters into geometrical input parameters. Further, the combined neural network and transfer function technique (neuro-TF) is used to model the EM responses w.r.t. the transformed geometrical variables. The model obtained using the proposed technique can achieve good accuracy with low complexity of neural networks, and further can be used in the high-level design. One tunable evanescent mode cavity filter example is used to demonstrate the validity of this technique.

Sayed Alireza Sadrossadat - One of the best experts on this subject based on the ideXlab platform.

  • Parametric Modeling of microwave passive components using sensitivity analysis based adjoint neural network technique
    IEEE Transactions on Microwave Theory and Techniques, 2013
    Co-Authors: Sayed Alireza Sadrossadat, Qijun Zhang
    Abstract:

    This paper presents a novel sensitivity-analysis-based adjoint neural-network (SAANN) technique to develop Parametric models of microwave passive components. This technique allows robust Parametric model development by learning not only the input–output behavior of the Modeling problem, but also derivatives obtained from electromagnetic (EM) sensitivity analysis. A novel derivation is introduced to allow complicated high-order derivatives to be computed by a simple artificial neural-network (ANN) forward-back propagation procedure. New formulations are deduced for exact second-order sensitivity analysis of general multilayer neural-network structures with any numbers of layers and hidden neurons. Compared to our previous work on adjoint neural networks, the proposed SAANN is easier to implement into an existing ANN structure. The proposed technique allows us to obtain accurate and Parametric models with less training data. Another benefit of this technique is that the trained model can accurately predict derivatives to geometrical or material parameters, regardless of whether or not these parameters are accommodated as sensitivity variables in EM simulators. Once trained, the SAANN models provide accurate and fast prediction of EM responses and derivatives used for high-level optimization with geometrical or material parameters as design variables. Three examples including Parametric Modeling of coupled-line filters, cavity filters, and junctions are presented to demonstrate the validity of this technique.