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

Abstract

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).

A comparative analysis of machine learning approach for optimizing antenna design

“…Different data-driven surrogating models [13] and inverse surrogate modeling [14] has been proposed for multiple different types of antenna [15,16] design to reduce the computational cost and accelerate the simulation process for optimizing and regularizing the antenna design process. In [17], the author has proposed a novel modeling techniques using small training data sets using surrogate antenna modeling to reduce cost. In [18], data-driven surrogate models like artificial intelligence algorithms, including deep learning algorithms, are used to design horn antenna to achieve a computationally efficient design and reduce the computational cost by more than 80%.…”

Computationally-efficient statistical design and yield optimization of resonator-based notch filters using feature-based surrogates

“…Consequently, the majority of practical robust design techniques involve fast surrogate models to expedite evaluation of the yield and its optimization. As indicated in " Introduction " section, behavioural models are typically used such as kriging, neural networks, or polynomial chaos expansion 20 , 22 , 31 , 34 , 49 . Notwithstanding, constructing reliable metamodels over multidimensional parameter space poses considerable challenges, and generally requires large amounts of training data, which is detrimental to the computational efficiency of the design process.…”

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

“…Efforts in space‐mapping/knowledge‐based modeling methods have also been expressed 6–14 to help EM parametric modeling and optimization perform better. Various space‐mapping methods have been introduced to map pre‐existing knowledge such as crude models, onto the EM response of microwave devices 15–49 …”