  We apply supervised machine learning techniques to a number of regression
problems in fluid dynamics. Four machine learning architectures are examined in
terms of their characteristics, accuracy, computational cost, and robustness
for canonical flow problems. We consider the estimation of force coefficients
and wakes from a limited number of sensors on the surface for flows over a
cylinder and NACA0012 airfoil with a Gurney flap. The influence of the temporal
density of the training data is also examined. Furthermore, we consider the use
of convolutional neural network in the context of super-resolution analysis of
two-dimensional cylinder wake, two-dimensional decaying isotropic turbulence,
and three-dimensional turbulent channel flow. In the concluding remarks, we
summarize on findings from a range of regression type problems considered
herein.
