Open AccessBayesian active learning for multi‐objective feasible region identification in microwave devices
Author(s) -
Garbuglia Federico,
Qing Jixiang,
Knudde Nicolas,
Spina Domenico,
Couckuyt Ivo,
Deschrijver Dirk,
Dhaene Tom
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12022
Subject(s) - computer science , bayesian optimization , identification (biology) , bayesian probability , set (abstract data type) , microwave , machine learning , artificial intelligence , telecommunications , botany , biology , programming language
In microwave device and circuit design, many simulations are often needed to find a set of designs that satisfy one or multiple specifications chosen by the designer upfront: the feasible region. A novel Bayesian active learning framework is presented to accurately identify the feasible region with a low number of simulations. The technique leverages on a stochastic model to obtain an efficient and automated procedure. A suitable application example validates the proposed technique and shows its effectiveness to rapidly obtain many suitable designs.