Wavefront reconstruction and prediction with convolutional neural networks

While deep learning has led to breakthroughs in many areas of computer science, its power has yet to be fully exploited in the area of adaptive optics (AO) and astronomy as a whole. In this paper we describe the first steps taken to apply deep, convolutional neural networks to the problem of wavefront reconstruction and prediction and demonstrate their feasibility of use in simulation. Our preliminary results show we are able to reconstruct wavefronts comparably well to current state of the art methods. We further demonstrate the ability to predict future wavefronts up to five simulation steps with under 1nm RMS wavefront error.