  Computational design optimization in fluid dynamics usually requires to solve
non-linear partial differential equations numerically. In this work, we explore
a Bayesian optimization approach to minimize an object's drag coefficient in
laminar flow based on predicting drag directly from the object shape. Jointly
training an architecture combining a variational autoencoder mapping shapes to
latent representations and Gaussian process regression allows us to generate
improved shapes in the two dimensional case we consider.
