  For natural guide start adaptive optics (AO) systems, pyramid wavefront
sensors (PWFSs) can provide significant increase in sensitivity over the
traditional Shack-Hartmann, but at the cost of a reduced linear range. When
using a linear reconstructor, non-linearities result in wavefront estimation
errors, which can have a significant impact on the image quality delivered by
the AO system. Here we simulate a wavefront passing through a PWFS under
varying observing conditions to explore the possibility of using a non-linear
machine learning model to estimate wavefront errors better than a linear
reconstruction. We find significant improvement even with light-weight models,
underscoring the need for further investigation of this approach.
