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Deep Learning for Aerodynamics: An Overview of Neural Network based Surrogate Modeling Capabilities of DLR SMARTy

mateus, dias ribeiro and Wassing, Simon and Hines Chaves, Derrick Armando and Bekemeyer, Philipp (2024) Deep Learning for Aerodynamics: An Overview of Neural Network based Surrogate Modeling Capabilities of DLR SMARTy. Machine Learning For Fluid Dynamics, Workshop, ERCOFTAC, 2024-03-06 - 2024-03-08, Paris, Frankreich.

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Item URL in elib:https://elib.dlr.de/210885/
Document Type:Conference or Workshop Item (Speech)
Title:Deep Learning for Aerodynamics: An Overview of Neural Network based Surrogate Modeling Capabilities of DLR SMARTy
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
mateus, dias ribeiroDLRUNSPECIFIEDUNSPECIFIED
Wassing, SimonUNSPECIFIEDhttps://orcid.org/0009-0008-4702-1358UNSPECIFIED
Hines Chaves, Derrick ArmandoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bekemeyer, PhilippUNSPECIFIEDhttps://orcid.org/0009-0001-9888-2499UNSPECIFIED
Date:2024
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Deep Learning, Neural Network, Surrogate Modeling, SMARTy
Event Title:Machine Learning For Fluid Dynamics, Workshop, ERCOFTAC
Event Location:Paris, Frankreich
Event Type:Workshop
Event Start Date:6 March 2024
Event End Date:8 March 2024
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Efficient Vehicle
DLR - Research area:Aeronautics
DLR - Program:L EV - Efficient Vehicle
DLR - Research theme (Project):L - Digital Technologies
Location: Braunschweig
Institutes and Institutions:Institute for Aerodynamics and Flow Technology > CASE, BS
Deposited By: Kiener, Anna
Deposited On:18 Dec 2024 10:46
Last Modified:02 Dec 2025 13:24

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