  A physics-based machine learning framework is developed to compute the
aerodynamic forces and moment for a pitching NACA0012 airfoil incurring in
light and deep dynamic stall. Three deep neural network frameworks of
increasing complexity are investigated: two multilayer perceptrons and a
convolutional neural network. The convolutional framework, in addition to the
standard mean squared error loss, features a physically-informed improved loss
function to compute the airfoil loads. In total, four models are investigated
of increasingly complexity. The convolutional model, coupled with the
physics-based loss function, is found to robustly and efficiently predict
pressure and skin friction distributions over the airfoil over the entire
pitching cycle. Periodic conditions are implemented to grant the physical
smoothness of the model output both in space and time. An analysis of the
training dataset point distributions is performed to point out the effects of
adopting low discrepancy sequences, such as Latin hypercube, Sobol', and
Halton, compared to random and uniform sequences. The current model shows
unprecedented performances in predicting forces and pitching moment in a broad
range of operating conditions.
