Real-Time Large Deformation Simulations Using Probabilistic Deep Learning Framework

Reference : Real-Time Large Deformation Simulations Using Probabilistic Deep Learning Framework


	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/10993/52829

Title :	Real-Time Large Deformation Simulations Using Probabilistic Deep Learning Framework
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 :	28-Jun-2022
Peer reviewed :	Yes
Audience :	International
Event name :	The Platform for Advanced Scientific Computing (PASC) Conference
Event date :	27-06-2022 to 28-06-2022
Keywords :	[en] Bayesian Deep learning ; Finite Element method ; real time simulation
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/52829
Other URL :	https://pasc22.pasc-conference.org/program/schedule/index.html%3Fpost_type=page&p=10&id=pos122&sess=sess181.html
Framework Programme / European Project :	H2020 ; 764644 - RAINBOW - Rapid Biomechanics Simulation for Personalized Clinical Design

File(s) associated to this reference

Fulltext file(s):

	File	Commentary	Version	Size	Access
Open access	PASC22_poster.pdf		Author postprint	5.04 MB	View/Open

Additional material(s):

	File	Commentary	Size	Access
Open access	PASC_video.mp4		30.32 MB	View/Open

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