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dc.contributor.authorCranney, Jesse
dc.contributor.authorZhang, Hao
dc.contributor.authorDoucet, Nicolas
dc.contributor.authorRigaut, François
dc.contributor.authorGratadour, Damien
dc.contributor.authorKorkiakoski, Visa
dc.contributor.authorDe Doná, José
dc.contributor.authorHong, Yuxi
dc.contributor.authorLtaief, Hatem
dc.contributor.authorKeyes, David E.
dc.date.accessioned2021-02-22T06:17:22Z
dc.date.available2021-02-22T06:17:22Z
dc.date.issued2020-12-13
dc.identifier.citationCranney, 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
dc.identifier.isbn9781510636835
dc.identifier.issn1996-756X
dc.identifier.issn0277-786X
dc.identifier.doi10.1117/12.2561914
dc.identifier.doi10.1117/12.2561913
dc.identifier.urihttp://hdl.handle.net/10754/667543
dc.description.abstractThe 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.
dc.publisherSPIE-Intl Soc Optical Eng
dc.relation.urlhttps://www.spiedigitallibrary.org/conference-proceedings-of-spie/11448/2561914/Predictive-learn-and-apply-MAVIS-application---apply/10.1117/12.2561914.full
dc.rightsArchived with thanks to SPIE
dc.titlePredictive learn and apply: MAVIS application-apply
dc.typeConference Paper
dc.typePoster
dc.typePresentation
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentOffice of the President
dc.conference.date2020-12-14 to 2020-12-22
dc.conference.nameAdaptive Optics Systems VII 2020
dc.conference.locationVirtual, Online, USA
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionResearch School of Astronomy and Astrophysics, College of Science, Australian National University, Australia
dc.contributor.institutionSchool of Electrical Engineering &Computing, Faculty of Engineering and Built Environment, University of Newcastle, Australia
dc.contributor.institutionLESIA, Observatoire de Paris, Université PSL, Sorbonne Université, Université de Paris, Sorbonne Paris Cité, CNRS France
dc.identifier.volume11448
kaust.personHong, Yuxi
kaust.personLtaief, Hatem
kaust.personKeyes, David E.
dc.identifier.eid2-s2.0-85100010237
refterms.dateFOA2021-02-22T06:20:13Z


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