  In this work, we propose a framework whereby flow imaging data is leveraged
to extract relevant information from flowfield visualizations. To this end, a
vision transformer (ViT) model is developed to predict the unsteady pressure
distribution over an airfoil under dynamic stall from images of the flowfield.
The network is capable of identifying relevant flow features present in the
images and associate them to the airfoil response. Results demonstrate that the
model is effective in interpolating and extrapolating between flow regimes and
for different airfoil motions, meaning that ViT-based models may offer a
promising alternative for sensors in experimental campaigns and for building
robust surrogate models of complex unsteady flows. In addition, we uniquely
treat the image semantic segmentation as an image-to-image translation task
that infers semantic labels of structures from the input images in a supervised
way. Given an input image of the velocity field, the resulting convolutional
neural network (CNN) generates synthetic images of any corresponding fluid
property of interest. In particular, we convert the velocity field data into
pressure in order to subsequently estimate the pressure distribution over the
airfoil in a robust manner.
