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Surrogate Modeling and Bayesian Optimization🔗

This practical session explores the use of Gaussian Processes in order to estimate a given physics phenomenon, say forces and torques on an aircraft's wing. Such a model is called a surrogate model of the actual phenomenon. This surrogate model is then used to optimize the mechanical characteristics of the airfoil, without resorting to costly numerical simulations or experiments. We explore methods for Bayesian optimization using surrogate models in the last part of the class.

Presentation
Notebook (colab)
Archive of notebook and solution files