Knowledge-based performance-driven modeling of antenna structures

Kozieł, Sławomir; Pietrenko‐Dabrowska, Anna

doi:10.1016/j.knosys.2021.107698

Cited by 18 publications

(38 citation statements)

References 69 publications

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“…An entirely different approach to handling dimensionality issues has been offered by the recently proposed performance-driven modeling frameworks 47 – 49 . The key concept of the methods belonging to this class is to restrict the metamodel domain to the most promising region of the parameter space, which encompasses the designs of high-quality with respect to the assumed performance figures.…”

Section: Introductionmentioning

confidence: 99%

“…Clearly, the acquisition of the reference designs incurred substantial computational expenditures, which, in some cases, could be justified by multiple use of the framework, e.g., for redesigning the structure at hand for different operating conditions. This expenses have been largely reduced (by a factor of sixty percent) in the further advancement of the technique, i.e., reference-design-free constrained modeling 49 , where the constrained domain has been determined using a preselected set of random observables. Acquisition of these observables does not require solving any optimization tasks.…”

Section: Introductionmentioning

confidence: 99%

“…Acquisition of these observables does not require solving any optimization tasks. Instead, a cost-efficient selection procedure is employed 49 , in which the decision on the acceptance of a specific observable is made using the knowledge extracted therefrom.…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes a novel surrogate modeling technique, which further improves the efficacy of the aforementioned reference-design-free constrained modeling method 49 by incorporating the response features technology 50 . The key concept of our approach is to handle the modeling task at the level of the system response features (characteristic points), rather than the frequency characteristics in their entirety.…”

Section: Introductionmentioning

confidence: 99%

“…In our approach, the surrogate domain is defined in a cost-efficient manner following methodology proposed in Ref. 49 , whereas the surrogate is set up at the level of the antenna response features, which allows for smoothing out the functional landscape to be approximated. Joint exploitation of both methodologies allows for constructing metamodels of antenna input characteristics using data set of truly low cardinalities without compromising the modeling accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Reduced-cost two-level surrogate antenna modeling using domain confinement and response features

Pietrenko‐Dabrowska

Kozieł

Ullah

2022

Sci Rep

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Electromagnetic (EM) simulation tools have become indispensable in the design of contemporary antennas. Still, the major setback of EM-driven design is the associated computational overhead. This is because a single full-wave simulation may take from dozens of seconds up to several hours, thus, the cost of solving design tasks that involve multiple EM analyses may turn unmanageable. This is where faster system representations (surrogates) come into play. Replacing expensive EM-based evaluations by cheap yet accurate metamodels seems to be an attractive solution. Still, in antenna design, application of surrogate models is hindered by the curse of dimensionality. A practical workaround has been offered by the recently reported reference-design-free constrained modeling techniques that restrict the metamodel domain to the parameter space region encompassing high-quality designs. Therein, the domain is established using only a handful of EM-simulations. This paper proposes a novel modeling technique, which incorporates the response feature technology into the constrained modeling framework. Our methodology allows for rendering accurate surrogates using exceptionally small training data sets, at the expense of reducing the generality of the modeling procedure to antennas that exhibit consistent shape of input characteristics. The proposed technique can be employed in other fields that employ costly simulation models (e.g., mechanical or aerospace engineering).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reduced-cost two-level surrogate antenna modeling using domain confinement and response features

Pietrenko‐Dabrowska

Kozieł

Ullah

2022

Sci Rep

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Advances in hybrid format‐based neuro‐transfer function techniques for parametric modeling of microwave components

Zhang

Liu

et al. 2023

Int J Numerical Modelling

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Electromagnetic (EM) parametric modeling has become significant for EM designs of microwave devices. This paper outlines recent advances in hybrid format‐based neuro‐transfer function (TF) techniques for EM parametric modeling of microwave devices. To solve the problem of high‐sensitivity, a novel decomposition approach is discussed to develop a rational‐based neuro‐TF model of EM behavior of microwave devices. To handle the issue of non‐smoothness and discontinuity, a parametric modeling technique incorporating pole‐residue/rational and neural network hybrid transfer function (short for rational/pole‐residue hybrid neuro‐TF) of EM behavior is reviewed. This technique effectively combines rational and residue‐pole formats of the transfer functions. Compared with the residue‐pole‐based neuro‐TF modeling approach and the rational‐based neuro‐TF modeling approach, the rational/pole‐residue hybrid neuro‐TF technique allows for better accuracy in large geometrical changes and high order applications. A parametric modeling method combining neural network and polynomial‐transfer function (neuro‐PTF) is further presented as an advanced version of the rational/pole‐residue hybrid neuro‐TF method. In this approach, the pole–residue‐based transfer function and the polynomial function are used together to describe the EM responses, and can produce more accurate models, especially with large geometrical variables. Following the modeling process, trained models can be used to provide fast and accurate EM response predictions and can subsequently be used for advanced circuit and system design.

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Low-Cost Behavioral Modeling of Antennas by Dimensionality Reduction and Domain Confinement

Koziel

Pietrenko‐Dabrowska

Leifsson

2023

Lecture Notes in Computer Science

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Knowledge-based performance-driven modeling of antenna structures

Cited by 18 publications

References 69 publications

Reduced-cost two-level surrogate antenna modeling using domain confinement and response features

Reduced-cost two-level surrogate antenna modeling using domain confinement and response features

Advances in hybrid format‐based neuro‐transfer function techniques for parametric modeling of microwave components

Low-Cost Behavioral Modeling of Antennas by Dimensionality Reduction and Domain Confinement

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