Data-driven constitutive laws for hyperelasticity in principal space using symbolic representations of Pytorch ANNs in FEniCS

Reference : Data-driven constitutive laws for hyperelasticity in principal space using symbolic r...


	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Engineering, computing & technology : Mechanical engineering Engineering, computing & technology : Multidisciplinary, general & others
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/47290

Title :	Data-driven constitutive laws for hyperelasticity in principal space using symbolic representations of Pytorch ANNs in FEniCS
Language :	English
Author, co-author :	Chau, Minh Vu [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Publication date :	21-May-2021
Number of pages :	A1
Peer reviewed :	No
Audience :	International
Event name :	DTU DRIVEN Colloquium
Event date :	21-05-2021
Event organizer :	Andreas Zilian
Event place (city) :	Esch-sur-Alzette
Event country :	Luxembourg
Keywords :	[en] Artificial Neural Network ; Computational data-driven ; Principal space
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	DRIVEN
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/47290
FnR project :	FnR ; FNR12252781 > Andreas Zilian > DRIVEN > Data-driven Computational Modelling And Applications > 01/09/2018 > 28/02/2025 > 2017

File(s) associated to this reference

Fulltext file(s):

	File	Commentary	Version	Size	Access
Open access	poster.pdf		Author preprint	1.23 MB	View/Open

All documents in ORBi^lu are protected by a user license.