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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230526T140000
DTEND;TZID=Asia/Seoul:20230526T160000
DTSTAMP:20260426T013948
CREATED:20230430T034034Z
LAST-MODIFIED:20230524T094243Z
UID:7650-1685109600-1685116800@www.ibs.re.kr
SUMMARY:Hyeontae Jo\,Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning
DESCRIPTION:We will discuss about “Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning”\, Zhao\, Shuai\, et al.\, IEEE Transactions on Power Electronics 37.10 (2022): 11567-11578. \nAbstract \nPhysics-informed machine learning (PIML) has been emerging as a promising tool for applications with domain knowledge and physical models. To uncover its potentials in power electronics\, this article proposes a PIML-based parameter estimation method demonstrated by a case study of dc–dc Buck converter. A deep neural network and the dynamic models of the converter are seamlessly coupled. It overcomes the challenges related to training data\, accuracy\, and robustness which a typical data-driven approach has. This exemplary application envisions to provide a new perspective for tailoring existing machine learning tools for power electronics.
URL:https://www.ibs.re.kr/bimag/event/2023-05-26-jc/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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