  Estimation of optical aberrations from volumetric intensity images is a key
step in sensorless adaptive optics for 3D microscopy. Recent approaches based
on deep learning promise accurate results at fast processing speeds. However,
collecting ground truth microscopy data for training the network is typically
very difficult or even impossible thereby limiting this approach in practice.
Here, we demonstrate that neural networks trained only on simulated data yield
accurate predictions for real experimental images. We validate our approach on
simulated and experimental datasets acquired with two different microscopy
modalities, and also compare the results to non-learned methods. Additionally,
we study the predictability of individual aberrations with respect to their
data requirements and find that the symmetry of the wavefront plays a crucial
role. Finally, we make our implementation freely available as open source
software in Python.
