A Python Toolbox for Data-Driven Aerodynamic Modeling Using Sparse Gaussian Processes

In aerodynamics, characterizing the aerodynamic behavior of aircraft typically requires a large number of observation data points. Real experiments can generate thousands of data points with suitable accuracy, but they are time-consuming and resource-intensive. Consequently, conducting real experime...

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Bibliographic Details
Published in:	Aerospace Vol. 11; no. 4; p. 260
Main Authors:	Valayer, Hugo, Bartoli, Nathalie, Castaño-Aguirre, Mauricio, Lafage, Rémi, Lefebvre, Thierry, López-Lopera, Andrés F., Mouton, Sylvain
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.04.2024 MDPI
Subjects:	Accuracy Aerodynamics Aircraft aerodynamics Analysis Approximation Complexity Data points Datasets Engineering Sciences Gaussian process Gaussian processes Parameter estimation Physics Random variables sparse methods surrogate modeling variational inference wind tunnel test Wind tunnels France surrogate modeling sparse methods wind tunnel test variational inference
ISSN:	2226-4310, 2226-4310
Online Access:	Get full text
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