Bayesian Machine Learning for Predicting Wing Pressure Distributions at Transonic Flow Conditions

What is it about?

In aircraft design, computational fluid dynamics plays an important role by providing invaluable insights into aerodynamic characteristics. Due to the significant computational cost, conducting the numerous evaluations required to cover the complete flight envelope becomes impractical. Alternatively, leveraging machine learning to construct surrogate models provides approaches to approximate quantities of interest within the design space.

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