Parallel Automatic Model Generation Technique for Microwave Modeling

Zhang, Lei; Cao, Yi; Wan, Shan Shan; Kabir, Humayun; Zhang, Qi‐Jun

doi:10.1109/mwsym.2007.380265

Cited by 15 publications

(13 citation statements)

References 9 publications

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“…1 forms the basis for every data based approximation methodology. For example, note the similarities with the work by Zhang in [26]. Of course, as-is, Fig.…”

Section: Global Behavioral Modelingmentioning

confidence: 62%

A Software Framework for Automated Behavioral Modeling of Electronic Devices [Application Notes]

et al. 2012

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Abstract-As the fabrication of prototypes is very costly, system-level computer simulations have become commonplace alternatives for the design of electronic devices and systems. However, due to the computational cost of these simulations, the use of behavioral modeling techniques has become indispensable. Behavioral models can act as a surrogate for the expensive simulations, and effectively speed-up the simulations without sacrificing accuracy. Such models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, there is great interest in techniques that facilitate the construction of behavioral models, while minimizing the computational cost and maximizing model accuracy. This paper explores how such models can be calculated in an automated way by means of a unified software framework that integrates adaptive modeling and adaptive sampling methods. It generates global behavioral models that are accurate and valid over the design space of interest while minimizing the number of electromagnetic simulations. By placing a strong focus on adaptivity, flexibility, and self-tuning the burden on the designer is relieved and the total modeling turn-around time is reduced.

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“…1 forms the basis for every data based approximation methodology. For example, note the similarities with the work by Zhang in [26]. Of course, as-is, Fig.…”

Section: Global Behavioral Modelingmentioning

confidence: 62%

A Software Framework for Automated Behavioral Modeling of Electronic Devices [Application Notes]

et al. 2012

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“…The parallel efficiency 1 is defined as the speed-up divided by the number of processors [37], that is…”

Section: Parallel Efficiency Of Distributed Model Evaluation Processmentioning

confidence: 99%

Distributed fine model evaluation for rapid space-mapping optimisation of microwave structures

Koziel

2009

IET Microw. Antennas Propag.

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A novel implementation of a space-mapping (SM) algorithm for optimisation of microwave structures and devices is described. The algorithm uses two techniques to speed up the SM optimisation process: the evaluation of the fine model is distributed through independent processing of the fine model response corresponding to consecutive frequency samples using a number of CPUs, and the parameter extraction and surrogate optimisation sub-problems are solved using built-in optimisation capabilities of the coarse model simulator. As a result, the optimisation time of microwave structures can be reduced to values comparable to or smaller than the time necessary for a single fine model evaluation on a single processor. This new implementation can be applied whenever the fine model is evaluated using a frequency-domain simulator. The robustness of this algorithm using microwave design optimisation problems is verified. The efficiency is compared with the standard implementation of the SM algorithm.

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“…The parallel efficiency ε is defined as the speed-up divided by the number of processors [12], i.e., ( 1) s n ε = + (6) For example, if we have 30 sub-bands and 8 processors, the speed-up is 7.5 and the parallel efficiency is about 94%.…”

Section: Distributed Evaluation Of the Fine Model In Smfmentioning

confidence: 99%

Space mapping with distributed fine model evaluation for optimization of microwave structures and devices

Koziel

2008

2008 IEEE MTT-S International Microwave Symposium Digest

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Abstract-A new space mapping optimization algorithm for microwave design is presented. We implement a distributed fine model evaluation through independent processing of the fine model responses corresponding to consecutive frequency samples using a number of processors. This allows us to obtain a substantial reduction of the overall optimization time for the space mapping algorithm. When our technique is used together with previously published methods of reducing the computational cost of solving the parameter extraction and surrogate optimization sub-problems, the total optimization time of the microwave structure can be comparable to or less than a single fine model evaluation on a single processor. Illustration examples are provided.Index Terms-Computer-aided design (CAD), EM optimization, space mapping, surrogate modeling, parallel model evaluation.

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Parallel Automatic Model Generation Technique for Microwave Modeling

Cited by 15 publications

References 9 publications

A Software Framework for Automated Behavioral Modeling of Electronic Devices [Application Notes]

A Software Framework for Automated Behavioral Modeling of Electronic Devices [Application Notes]

Distributed fine model evaluation for rapid space-mapping optimisation of microwave structures

Space mapping with distributed fine model evaluation for optimization of microwave structures and devices

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