I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Ensemble Machine Learning Methods for Unsteady Aerodynamics..:
, In:
2024 10th International Conference on Control, Automation and Robotics (ICCAR)
,
Kumar, Ajit
;
Ghosh, Ajoy Kanti
- p. 219-225 , 2024
Link:
https://doi.org/10.1109/ICCAR61844.2024.10569721
RT T1 <cTitle>2024 10th International Conference on Control, Automation and Robotics (ICCAR)</cTitle>: T1 <Title>Ensemble Machine Learning Methods for Unsteady Aerodynamics Modeling from Flight Data</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10569721&Exemplar=1&LAN=DE A1 Kumar, Ajit A1 Ghosh, Ajoy Kanti YR 2024 SN 2251-2454 K1 Maximum likelihood estimation K1 Machine learning algorithms K1 Force K1 Predictive models K1 Aerodynamics K1 Boosting K1 Mathematical models K1 Ensemble learning K1 Bagging K1 Unsteady Aerodynamic Modeling K1 Flight test data SP 219 OP 225 LK http://dx.doi.org/https://doi.org/10.1109/ICCAR61844.2024.10569721 DO https://doi.org/10.1109/ICCAR61844.2024.10569721 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
