Author: Burger, M.
Paper Title Page
THPAB349 Feed-Forward Neural Network Based Modelling of an Ultrafast Laser for Enhanced Control 4478
 
  • A. Aslam, M. Martínez-Ramón, S.D. Scott
    UNM-ECE, Albuquerque, USA
  • S. Biedron
    Argonne National Laboratory, Office of Naval Research Project, Argonne, Illinois, USA
  • S. Biedron
    Element Aero, Chicago, USA
  • S. Biedron
    UNM-ME, Albuquerque, New Mexico, USA
  • M. Burger, J. Murphy
    NERS-UM, Ann Arbor, Michigan, USA
  • K.M. Krushelnick, J. Nees, A.G.R. Thomas
    University of Michigan, Ann Arbor, Michigan, USA
  • Y. Ma
    IHEP, Beijing, People’s Republic of China
  • Y. Ma
    Michigan University, Ann Arbor, Michigan, USA
  
  Funding: Acknowledgements: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under award number DE-SC0019468.
The ap­pli­ca­tions of ma­chine learn­ing in today’s world en­com­pass all fields of life and phys­i­cal sci­ences. In this paper, we im­ple­ment a ma­chine learn­ing based al­go­rithm in the con­text of laser physics and par­ti­cle ac­cel­er­a­tors. Specif­i­cally, a neural net­work-based op­ti­mi­sa­tion al­go­rithm has been de­vel­oped that of­fers en­hanced con­trol over an ul­tra­fast fem­tosec­ond laser in com­par­i­son to the tra­di­tional Pro­por­tional In­te­gral and de­riv­a­tive (PID) con­trols. This re­search opens a new po­ten­tial of util­is­ing ma­chine learn­ing and even deep learn­ing tech­niques to im­prove the per­for­mance of sev­eral dif­fer­ent lasers and ac­cel­er­a­tors sys­tems. 		 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB349  
About • paper received ※ 20 May 2021       paper accepted ※ 02 July 2021       issue date ※ 17 August 2021  
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