Unconstrained high dimensional Bayesian optimization of an airfoil shape design via multi fidelity surrogate modeling
In engineering, science, and technology, predictions and design decisions can be made or informed by a variety of information sources that range from experimental data to computer models. These information sources typically encompass different mathematical formulations, different grid resolutions, different physics, or different modeling assumptions that simplify the problem. This leads to information sources with varying degrees of fidelity, with varying associated accuracy and querying costs. In this paper we propose a novel and flexible way to use multifidelity information sources optimally in the context of airfoil shape optimization using both a Reynolds averaged Navier Stokes (RANS) solver and a low fidelity approximation based on a simplified physical formulation. The new developments based on Bayesian optimization and kriging metamodeling allow the aerodynamic optimization to be sped up and divide for example (on a 15 design variable unconstrained optimization problem) the total cost by at least two compared to a single fidelity optimization.