  We compute, model, and predict drag reduction of an actuated turbulent
boundary layer at a momentum thickness based Reynolds number of Re{\theta} =
1000. The actuation is performed using spanwise traveling transversal surface
waves parameterized by wavelength, amplitude, and period. The drag reduction
for the set of actuation parameters is modeled using 71 large-eddy simulations
(LES). This drag model allows to extrapolate outside the actuation domain for
larger wavelengths and amplitudes. The modeling novelty is based on combining
support vector regression for interpolation, a parameterized ridgeline leading
out of the data domain, scaling from Tomiyama and Fukagata (2013), and a
discovered self-similar structure of the actuation effect. The model yields
high prediction accuracy outside the training data range.
