About: Reduced-Cost Constrained Modeling of Microwave and Antenna Components: Recent Advances

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Reduced-Cost Constrained Modeling of Microwave and Antenna Components: Recent Advances
Creator	Koziel, Slawomir Leifsson, Leifur Pietrenko-Dabrowska, Anna
source	PMC
abstract	Electromagnetic (EM) simulation models are ubiquitous in the design of microwave and antenna components. EM analysis is reliable but CPU intensive. In particular, multiple simulations entailed by parametric optimization or uncertainty quantification may considerably slow down the design processes. In order to address this problem, it is possible to employ fast metamodels. Here, the popular solution approaches are approximation surrogates, which are versatile and easily accessible. Notwithstanding, the major issue for conventional modeling methods is the curse of dimensionality. In the case of high-frequency components, an added difficulty are highly nonlinear outputs that need to be handled. A recently reported constrained modeling attempts to broaden the applicability of approximation surrogates by confining the surrogate model setup to a small subset of the parameter space. The said region contains the parameter vectors corresponding to high-quality designs w.r.t. the considered figures of interest, which allows for a dramatic reduction of the number of training samples needed to render reliable surrogates without formally restricting the parameter ranges. This paper reviews the recent techniques employing these concepts and provides real-world illustration examples of antenna and microwave structures.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50426-7_4
has license	no-cc
sha1sum (hex)	e175d2739c6f2d5682c201392ca0c6f6d1dbceb5
schema:url	https://doi.org/10.1007/978-3-030-50426-7_4
resource representing a document's title	Reduced-Cost Constrained Modeling of Microwave and Antenna Components: Recent Advances
has PubMed Central identifier	PMC7302561
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:e175d2739c6f2d5682c201392ca0c6f6d1dbceb5#body_text
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