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Enhanced uniform data sampling for constrained data‐driven modeling of antenna input characteristics
Author(s) -
Koziel Slawomir,
Sigurðsson Ari T.,
PietrenkoDabrowska Anna,
Szczepanski Stanislaw
Publication year - 2019
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2584
Subject(s) - curse of dimensionality , computer science , sampling (signal processing) , domain (mathematical analysis) , antenna (radio) , process (computing) , set (abstract data type) , sample (material) , power (physics) , manifold (fluid mechanics) , transformation (genetics) , algorithm , mathematical optimization , mathematics , artificial intelligence , telecommunications , engineering , mechanical engineering , mathematical analysis , biochemistry , chemistry , physics , chromatography , quantum mechanics , detector , gene , programming language , operating system
Data‐driven surrogates are the most popular replacement models utilized in many fields of engineering and science, including design of microwave and antenna structures. The primary practical issue is a curse of dimensionality, which limits the number of independent parameters that can be accounted for in the modeling process. Recently, a performance‐driven modeling technique has been proposed where the constrained domain of the model is spanned by a set of reference designs optimized with respect to selected figures of interest. This approach allows for significant improvement of prediction power of the surrogates without the necessity of reducing the parameter ranges. Yet uniform allocation of the training data samples in the constrained domain remains a problem. Here, a novel design of experiments technique ensuring better sample uniformity is proposed. Our approach involves uniform sampling on the domain‐spanning manifold and linear transformation of the remaining sample vector components onto orthogonal directions with respect to the manifold. Two antenna examples are provided to demonstrate the advantages of the technique, including application case studies (antenna optimization).