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    Predictive learn and apply: MAVIS application-apply

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    Type
    Conference Paper
    Poster
    Presentation
    Authors
    Cranney, Jesse
    Zhang, Hao
    Doucet, Nicolas
    Rigaut, François
    Gratadour, Damien
    Korkiakoski, Visa
    De Doná, José
    Hong, Yuxi cc
    Ltaief, Hatem cc
    Keyes, David E. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer Science
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Extreme Computing Research Center
    Office of the President
    Date
    2020-12-13
    Permanent link to this record
    http://hdl.handle.net/10754/667543
    
    Metadata
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    Abstract
    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:10.1117/12.2561914
    Publisher
    SPIE-Intl Soc Optical Eng
    Conference/Event name
    Adaptive Optics Systems VII 2020
    ISBN
    9781510636835
    DOI
    10.1117/12.2561914
    10.1117/12.2561913
    Additional Links
    https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11448/2561914/Predictive-learn-and-apply-MAVIS-application---apply/10.1117/12.2561914.full
    ae974a485f413a2113503eed53cd6c53
    10.1117/12.2561914
    Scopus Count
    Collections
    Conference Papers; Applied Mathematics and Computational Science Program; Extreme Computing Research Center; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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