  Global aerodynamic design optimization using Euler or Navier-Stokes equations
requires very reliable surrogate modeling techniques since the computational
effort for the underlying flow simulations is usually high. In general, for
such problems, the number of samples that can be generated to train the
surrogate models is very limited due to restricted computational resources. On
the other hand, recent developments in adjoint methods enable nowadays
evaluation of gradient information at a reasonable computational cost for a
wide variety of engineering problems. Therefore, a much richer data set can be
collected using an adjoint solver in a Design of Experiment framework. In the
present work, we present a novel aggregation method, which enables the state of
the art surrogate models to incorporate extra gradient information without
causing overfitting problems. Therefore, accurate surrogate models with
relatively large number of design parameters can be trained from a small set of
samples. We also present results two well known benchmark design optimization
problems showing efficiency and robustness of the new method.
