Novel deep learning approaches for learning scientific simulations

Reference : Novel deep learning approaches for learning scientific simulations


	Document type :	Scientific congresses, symposiums and conference proceedings : Unpublished conference
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/55478

Title :	Novel deep learning approaches for learning scientific simulations
Language :	English
Author, co-author :	Deshpande, Saurabh [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
	Sosa, Raul Ian [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS) >]
	Bordas, Stéphane [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
	Lengiewicz, Jakub [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Publication date :	Aug-2023
Peer reviewed :	Yes
Audience :	International
Event name :	The 14th International Conference of Computational Methods (ICCM2023)
Event date :	06-08-2023 to 10-08-2023
Event place (city) :	Ho Chi Minh
Event country :	Vietnam
Keywords :	[en] Non-linear FEM ; Deep learning ; Surrogate modeling
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/55478
Framework Programme / European Project :	H2020 ; 764644 - RAINBOW - Rapid Biomechanics Simulation for Personalized Clinical Design

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