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@INPROCEEDINGS{Stein:478846,
      author       = {Stein, Oliver and Agapov, Ilya and Eichler, Annika and
                      Kaiser, Jan},
      title        = {{A}ccelerating {L}inear {B}eam {D}ynamics {S}imulations for
                      {M}achine {L}earning {A}pplications},
      address      = {[Geneva]},
      publisher    = {JACoW Publishing, Geneva, Switzerland},
      reportid     = {PUBDB-2022-02796},
      isbn         = {978-3-95450-227-1},
      pages        = {2330-2333},
      year         = {2022},
      note         = {Literaturangaben;},
      comment      = {[Ebook] 13th International Particle Accelerator Conference
                      : June 12-17, 2022, Impact Forum, Muangthong Thani, Bangkok,
                      Thailand : conference proceedings / Chanwattana, Thakonwat ,
                      [Geneva] : JACoW Publishing, July 2022,},
      booktitle     = {[Ebook] 13th International Particle
                       Accelerator Conference : June 12-17,
                       2022, Impact Forum, Muangthong Thani,
                       Bangkok, Thailand : conference
                       proceedings / Chanwattana, Thakonwat ,
                       [Geneva] : JACoW Publishing, July
                       2022,},
      abstract     = {Machine learning has proven to be a powerful tool with many
                      applications in the field of accelerator physics. Training
                      machine learning models is a highly iterative process that
                      requires large numbers of samples. However, beam time is
                      often limited and many of the available simulation
                      frameworks are not optimized for fast computation. As a
                      result, training complex models can be infeasible. In this
                      contribution, we introduce Cheetah, a linear beam dynamics
                      framework optimized for fast computations. We show that
                      Cheetah outperforms existing simulation codes in terms of
                      speed and furthermore demonstrate the application of Cheetah
                      to a reinforcement-learning problem as well as the
                      successful transfer of the Cheetah-trained model to the real
                      world. We anticipate that Cheetah will allow for faster
                      development of more capable machine learning solutions in
                      the field, one day enabling the development of autonomous
                      accelerators.},
      month         = {Jun},
      date          = {2022-06-12},
      organization  = {13th International Particle
                       Accelerator Conference, Bangkok
                       (Thailand), 12 Jun 2022 - 17 Jun 2022},
      keywords     = {Accelerator Physics (Other) / MC5: Beam Dynamics and EM
                      Fields (Other) / simulation (autogen) / space-charge
                      (autogen) / controls (autogen) / GPU (autogen) / experiment
                      (autogen)},
      cin          = {MSK / MPY / D3},
      cid          = {I:(DE-H253)MSK-20120731 / I:(DE-H253)MPY-20120731 /
                      I:(DE-H253)D3-20120731},
      pnm          = {621 - Accelerator Research and Development (POF4-621) /
                      ZT-I-PF-5-6 - Autonomous Accelerator (AA)
                      $(2020_ZT-I-PF-5-6)$},
      pid          = {G:(DE-HGF)POF4-621 / $G:(DE-HGF)2020_ZT-I-PF-5-6$},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.18429/JACoW-IPAC2022-WEPOMS036},
      url          = {https://bib-pubdb1.desy.de/record/478846},
}