Bayesian active learning for multi‐objective feasible region identification in microwave devices

Autor:	Federico Garbuglia, Jixiang Qing, Nicolas Knudde, Domenico Spina, Ivo Couckuyt, Dirk Deschrijver, Tom Dhaene
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Computer-aided circuit analysis and design Microwave circuits and devices Other topics in statistics Electronic engineering computing Machine learning (artificial intelligence) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Zdroj:	Electronics Letters, Vol 57, Iss 10, Pp 400-403 (2021)
Druh dokumentu:	article
ISSN:	1350-911X 0013-5194
DOI:	10.1049/ell2.12022
Popis:	Abstract 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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/04d412d0ce904fd5a21803d7278031fa Zobrazit plný text záznamu Plný text View record in DOAJ