Presentation + Paper
17 July 2018 Wavefront reconstruction and prediction with convolutional neural networks
Author Affiliations +
Abstract
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.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robin Swanson, Masen Lamb, Carlos Correia, Suresh Sivanandam, and Kiriakos Kutulakos "Wavefront reconstruction and prediction with convolutional neural networks", Proc. SPIE 10703, Adaptive Optics Systems VI, 107031F (17 July 2018); https://doi.org/10.1117/12.2312590
Lens.org Logo
CITATIONS
Cited by 10 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Wavefronts

Wavefront reconstruction

Adaptive optics

Data modeling

Atmospheric modeling

Convolutional neural networks

Computer programming

Back to Top