Type
Conference PaperPoster
Presentation
Authors
Cranney, JesseZhang, Hao
Doucet, Nicolas
Rigaut, François
Gratadour, Damien
Korkiakoski, Visa
De Doná, José
Hong, Yuxi

Ltaief, Hatem

Keyes, David E.

KAUST Department
Applied Mathematics and Computational Science ProgramComputer Science
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Extreme Computing Research Center
Office of the President
Date
2020-12-13Permanent link to this record
http://hdl.handle.net/10754/667543
Metadata
Show full item recordAbstract
The Learn and Apply tomographic reconstructor coupled with the pseudo open-loop control scheme shows promising results in simulation for multi-conjugate adaptive optics systems. We motivate, derive, and demonstrate the inclusion of a predictive step in the Learn and Apply tomographic reconstructor based on frozen-flow turbulence assumption. The addition of this predictive step provides an additional gain in performance, especially at larger wave-front sensor exposure periods, with no increase of online computational burden. We provide results using end-to-end numerical simulations for a multi-conjugate adaptive optics system for an 8m telescope based on the MAVIS system design.Citation
Cranney, J., Zhang, H., Doucet, N., Rigaut, F., Gratadour, D., Korkiakoski, V. A., … Keyes, D. E. (2020). Predictive learn and apply: MAVIS application - apply. Adaptive Optics Systems VII. doi:10.1117/12.2561914Publisher
SPIE-Intl Soc Optical EngConference/Event name
Adaptive Optics Systems VII 2020ISBN
9781510636835ae974a485f413a2113503eed53cd6c53
10.1117/12.2561914