Novel Geometric Deep Learning Surrogate Framework for Non-Linear Finite Element Simulations

Reference : Novel Geometric Deep Learning Surrogate Framework for Non-Linear Finite Element Simul...


	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Engineering, computing & technology : Multidisciplinary, general & others
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/55643

Title :	Novel Geometric Deep Learning Surrogate Framework for Non-Linear Finite Element Simulations
Language :	English
Author, co-author :	Deshpande, Saurabh [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) >]
	Bordas, Stéphane [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Publication date :	27-Jun-2023
Peer reviewed :	Yes
Event name :	The Platform for Advanced Scientific Computing (PASC) Conference 2023
Event date :	26-06-2023 to 28-06-2023
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/55643
Framework Programme / European Project :	H2020 ; 764644 - RAINBOW - Rapid Biomechanics Simulation for Personalized Clinical Design

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	File	Commentary	Version	Size	Access
Open access	PASC23_poster_magnet-3.pdf		Author postprint	12.09 MB	View/Open

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