Variable‐fidelity modeling of antenna input characteristics using domain confinement and two‐stage Gaussian process regression surrogates

Pieter Jacobs; Sławomir Kozieł

doi:10.1002/jnm.2758

Variable‐fidelity modeling of antenna input characteristics using domain confinement and two‐stage Gaussian process regression surrogates

Abstrakt

The major bottleneck of electromagnetic (EM)-driven antenna design is the high CPU cost of massive simulations required by parametric optimization, uncertainty quantification, or robust design procedures. Fast surrogate models may be employed to mitigate this issue to a certain extent. Unfortunately, the curse of dimensionality is a serious limiting factor, hindering the construction of conventional data-driven models valid over wide ranges of the antenna parameters and operating conditions. This paper proposes a novel surrogate modeling approach that capitalizes on two recently proposed frameworks: the nested kriging approach and two-stage Gaussian process regression (GPR). In our methodology, the first-level surrogate of nested kriging is applied to define the confined domain of the model in which the final surrogate is constructed using two-stage GPR. The latter permits blending information from a sparsely-sampled high-fidelity EM simulation model and a densely-sampled low-fidelity (or coarse-mesh) model. This combination enables significant computational savings in terms of training data acquisition while retaining excellent predictive power of the surrogate. At the same time, the proposed framework inherits all the benefits of nested kriging, including ease of uniform sampling of the confined domain as well as straightforward generation of a good initial design for surrogate model optimization. Comprehensive benchmarking carried out using two antenna examples demonstrates superiority of our technique over conventional surrogates (unconfined domain), and standard GPR applied to the confined domain. Application examples for antenna optimization are also provided.

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Autorzy (2)

Pieter Jacobs
Sławomir Kozieł prof. dr inż.

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Wersja publikacji: Accepted albo Published Version
Licencja: Copyright (2020 John Wiley & Sons, Ltd)

Słowa kluczowe

ANTENNA DESIGN, SURROGATE MODELING, DATA-DRIVEN MODELS, GAUSSIAN PROCESS REGRESSION, VARIABLE-FIDELITY SIMULATIONS

Informacje szczegółowe

Kategoria:: Publikacja w czasopiśmie
Typ:: artykuły w czasopismach
Opublikowano w:: INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS nr 33,
ISSN: 0894-3370
Język:: angielski
Rok wydania:: 2020
Opis bibliograficzny:: Jacobs P., Kozieł S.: Variable‐fidelity modeling of antenna input characteristics using domain confinement and two‐stage Gaussian process regression surrogates// INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS -Vol. 33,iss. 6 (2020), s.2758-
DOI:: Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1002/jnm.2758
Weryfikacja:: Politechnika Gdańska