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Machine Learning for Aerodynamic Uncertainty Quantification

Liu, Dishi and Maruyama, Daigo and Görtz, Stefan (2020) Machine Learning for Aerodynamic Uncertainty Quantification. In: ERCIM News Special Theme "Solving Engineering Problems with Machine Learning" (122). pp. 20-21. ISSN 0926-4981.

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Official URL: https://ercim-news.ercim.eu/images/stories/EN122/EN122-web.pdf


Within the framework of the project "Uncertainty Management for Robust Industrial Design in Aeronautics" (UMRIDA), funded by the European Union, several machine learning-based predictive models were compared in terms of their efficiency in estimating statistics of aerodynamic performance of aerofoils. The results show that the models based on both samples and gradients achieve better accuracy than those based solely on samples at the same computational costs.

Item URL in elib:https://elib.dlr.de/135557/
Document Type:Contribution to a Collection
Title:Machine Learning for Aerodynamic Uncertainty Quantification
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Liu, DishiDishi.Liu (at) dlr.deUNSPECIFIED
Maruyama, DaigoDaigo.Maruyama (at) dlr.deUNSPECIFIED
Görtz, StefanStefan.Goertz (at) dlr.deUNSPECIFIED
Date:July 2020
Journal or Publication Title:ERCIM News
Refereed publication:No
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 20-21
EditorsEmailEditor's ORCID iD
Kunz, Peterpeter.kunz@ercim.euUNSPECIFIED
Series Name:Special Theme "Solving Engineering Problems with Machine Learning"
Keywords:CFD, aerodynamics, uncertainty
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 (old)
Location: Braunschweig
Institutes and Institutions:Institute for Aerodynamics and Flow Technology > CASE, BS
Deposited By: Görtz, Stefan
Deposited On:08 Sep 2020 07:31
Last Modified:20 Jun 2021 15:53

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