A Python Toolbox for Data-Driven Aerodynamic Modeling Using Sparse Gaussian Processes
Hugo Valayer,
Nathalie Bartoli,
Mauricio Castaño-Aguirre
et al.
Abstract: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 experiments at new input configurations might be impractical. To address this challenge, data-driven surrogate models have emerged as a cost-effective and time-efficient alternative. They provide simplified mathematical rep… Show more
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