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A set-based approach to dynamic system design using physics..:
Kohei SHINTANI
;
Eiji NAKATSUGAWA
;
Minoru TSUCHIYAMA
https://www.jstage.jst.go.jp/article/jamdsm/16/5/16_2022jamdsm0051/_pdf/-char/en. , 2022
Link:
https://doi.org/10.1299/jamdsm.2022jamdsm0051
RT Journal T1 <Title>A set-based approach to dynamic system design using physics informed neural network</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:dc34f3021f044118843661b35c45ec18&Exemplar=1&LAN=DE A1 Kohei SHINTANI A1 Eiji NAKATSUGAWA A1 Minoru TSUCHIYAMA PB The Japan Society of Mechanical Engineers YR 2022 K1 gaussian process K1 bayesian active learning K1 physics-informed neural network K1 long short-term memory (lstm) K1 design optimization K1 surrogate modeling K1 Engineering machinery K1 tools K1 and implements K1 TA213-215 K1 Mechanical engineering and machinery K1 TJ1-1570 JF https://www.jstage.jst.go.jp/article/jamdsm/16/5/16_2022jamdsm0051/_pdf/-char/en LK http://dx.doi.org/https://doi.org/10.1299/jamdsm.2022jamdsm0051 DO https://doi.org/10.1299/jamdsm.2022jamdsm0051 SF ELIB - SuUB Bremen
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