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Autonomous and Reliable Aeroelastic System Identification using Density-based Clustering, Gaussian Processes and Kalman Filtering

Volkmar, Robin (2023) Autonomous and Reliable Aeroelastic System Identification using Density-based Clustering, Gaussian Processes and Kalman Filtering. Advances in Artificial Intelligence for Aerospace Engineering at Onera DLR Aerospace Symposium, 2023-05-30, Paris, Frankreich.

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Item URL in elib:https://elib.dlr.de/195964/
Document Type:Conference or Workshop Item (Speech)
Title:Autonomous and Reliable Aeroelastic System Identification using Density-based Clustering, Gaussian Processes and Kalman Filtering
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Volkmar, RobinUNSPECIFIEDhttps://orcid.org/0000-0002-5920-0686UNSPECIFIED
Date:2023
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:ground vibration test, flight vibration test, clustering, Gaussian processes, Kalman filter
Event Title:Advances in Artificial Intelligence for Aerospace Engineering at Onera DLR Aerospace Symposium
Event Location:Paris, Frankreich
Event Type:Workshop
Event Date:30 May 2023
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Components and Systems
DLR - Research area:Aeronautics
DLR - Program:L CS - Components and Systems
DLR - Research theme (Project):L - Aircraft Systems
Location: Göttingen
Institutes and Institutions:Institute of Aeroelasticity > Structural Dynamics and System Identification
Deposited By: Volkmar, Robin
Deposited On:17 Jul 2023 11:42
Last Modified:24 Apr 2024 20:56

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