@inproceedings{509dc81aecdc44bcb498142f2d844172,
title = "Predictive learn and apply: MAVIS application-learn",
abstract = "The Learn and Apply reconstruction scheme uses the knowledge of atmospheric turbulence to generate a tomographic reconstructor, and its performance is enhanced by the real-time identification of the atmosphere and the wind profile. In this paper we propose a turbulence profiling method that is driven by the atmospheric model. The vertical intensity distribution of turbulence, wind speed and wind direction can be simultaneously estimated from the Laser Guide Star measurements. We introduce the implementation of such a method on a GPU accelerated non-linear least-squares solver, which significantly increases the computation efficiency. Finally, we present simulation results to demonstrate the convergence quality from numerically generated telemetry, the end-to-end Adaptive Optics simulation results, and a time-to-solution analysis, all based on the MAVIS system design.",
keywords = "Adaptive optics, Learn and Apply, MAVIS, Real-time processing, Stochastic Levenberg-Marquardt, Turbulence profiling",
author = "Hao Zhang and Jesse Cranney and Nicolas Doucet and Yuxi Hong and Damien Gratadour and Hatem Ltaief and David Keyes and Fran{\c c}ois Rigaut",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE.; Adaptive Optics Systems VII 2020 ; Conference date: 14-12-2020 Through 22-12-2020",
year = "2020",
doi = "10.1117/12.2561913",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Laura Schreiber and Dirk Schmidt and Elise Vernet",
booktitle = "Adaptive Optics Systems VII",
address = "United States",
}