An evolutionary physics-informed deep learning model for a prediction of physical fields at an arbitrary time: Case study on transient heat conduction

dc.contributor.advisor	村松, 眞由 / 准教授
dc.contributor.author	YAMAZAKI, YUSUKE / 山嵜, 祐輔
dc.date.accessioned	2025-06-20T01:07:43Z
dc.date.available	2025-06-20T01:07:43Z
dc.date.issued	2024-03-26
dc.description	修士（工学）, 2023, 開放環境科学専攻
dc.identifier.uri	http://131.113.16.178:8181/sigma_local/handle/10721/15286
dc.language	en
dc.publisher	慶應義塾大学理工学研究科
dc.subject	有限要素法	ja
dc.subject	熱伝導	ja
dc.subject	Physics-informed deep learning	en
dc.subject	Finite element method	en
dc.subject	Heat conduction	en
dc.subject	Operator learning	en
dc.title	An evolutionary physics-informed deep learning model for a prediction of physical fields at an arbitrary time: Case study on transient heat conduction
dc.title.alternative	Physics-informed neural networkを援用した任意時間における物理場を予測する深層学習モデルの構築：過度熱伝導問題における検討
dc.type	学位論文

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