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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Vydané v:	Aerospace Ročník 11; číslo 4; s. 260
Hlavní autori:	Valayer, Hugo, Bartoli, Nathalie, Castaño-Aguirre, Mauricio, Lafage, Rémi, Lefebvre, Thierry, López-Lopera, Andrés F., Mouton, Sylvain
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.04.2024 MDPI
Predmet:	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
On-line prístup:	Získať plný text
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