  RANS simulations with the Spalart-Allmaras turbulence model are improved for
cases with flow separation using the Field Inversion and Machine Learning
approach. A compensatory discrepancy term is introduced into the turbulence
model and optimized using high-fidelity reference data from experiments.
Influences on the optimization results with respect to regularization, grid
resolution and areas in which the optimization is active are investigated.
Finally, a neural network is trained and used to augment simulations on a test
case.
