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Efficient quantification of geometric uncertainties using gradient-assisted surrogate models and Karhunen-Loeve expansion

Liu, Dishi and Stefan, Görtz (2015) Efficient quantification of geometric uncertainties using gradient-assisted surrogate models and Karhunen-Loeve expansion. UMRIDA UQ-Workshop 2015, 15-16 Apr 2015, Delft, Netherlands.

Full text not available from this repository.


Item URL in elib:https://elib.dlr.de/99525/
Document Type:Conference or Workshop Item (Speech)
Title:Efficient quantification of geometric uncertainties using gradient-assisted surrogate models and Karhunen-Loeve expansion
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Liu, Dishidishi.liu (at) dlr.deUNSPECIFIED
Stefan, Görtzstefan.goertz (at) dlr.deUNSPECIFIED
Date:15 April 2015
Refereed publication:No
Open Access:No
In DOAJ:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:uncertainty quantification, surrogate model
Event Title:UMRIDA UQ-Workshop 2015
Event Location:Delft, Netherlands
Event Type:Workshop
Event Dates:15-16 Apr 2015
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:fixed-wing aircraft
DLR - Research area:Aeronautics
DLR - Program:L AR - Aircraft Research
DLR - Research theme (Project):L - Simulation and Validation
Location: Braunschweig
Institutes and Institutions:Institute of Aerodynamics and Flow Technology > C²A²S²E - Center for Computer Applications in AeroSpace Science and Engineering
Deposited By: Liu, Dishi
Deposited On:18 Nov 2015 11:24
Last Modified:23 May 2016 22:55

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