  Computational Fluid Dynamics (CFD) is a major sub-field of engineering.
Corresponding flow simulations are typically characterized by heavy
computational resource requirements. Often, very fine and complex meshes are
required to resolve physical effects in an appropriate manner. Since all CFD
algorithms scale at least linearly with the size of the underlying mesh
discretization, finding an optimal mesh is key for computational efficiency.
  One methodology used to find optimal meshes is goal-oriented adaptive mesh
refinement. However, this is typically computationally demanding and only
available in a limited number of tools. Within this contribution, we adopt a
machine learning approach to identify optimal mesh densities. We generate
optimized meshes using classical methodologies and propose to train a
convolutional network predicting optimal mesh densities given arbitrary
geometries. The proposed concept is validated along 2d wind tunnel simulations
with more than 60,000 simulations. Using a training set of 20,000 simulations
we achieve accuracies of more than 98.7%.
  Corresponding predictions of optimal meshes can be used as input for any mesh
generation and CFD tool. Thus without complex computations, any CFD engineer
can start his predictions from a high quality mesh.
