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@PHDTHESIS{Kaiser:646123,
      author       = {Kaiser, Jan},
      othercontributors = {Eichler, Annika and Fey, Görschwin},
      title        = {{R}einforcement {L}earning and {D}ifferentiable
                      {S}imulations for {A}utonomous {T}uning and {C}ontrol of
                      {L}inear {P}article {A}ccelerators},
      school       = {Technische Universität Hamburg},
      type         = {Dissertation},
      address      = {München},
      publisher    = {Dr. Hut Verlag},
      reportid     = {PUBDB-2026-00725},
      isbn         = {978-3-8439-5683-3},
      series       = {Regelungstechnik},
      pages        = {231},
      year         = {2025},
      note         = {Sonstige Körperschaft: Technische Universität Hamburg,
                      Institute of Control Systems;; Dissertation, Technische
                      Universität Hamburg, 2025},
      abstract     = {Particle accelerators are sophisticated scientific
                      facilities that require precise but time-consuming
                      optimisation to achieve optimal performance. Considering
                      benchmark tasks at the ARES and LCLS facilities, this
                      dissertation proposes methods to deploy simulation-trained
                      reinforcement learning (RL) policies for accelerator tuning
                      zero-shot to the real world and novel tuning tasks, while
                      comparing their performance to traditional methods. A
                      high-speed differentiable beam dynamics simulator is
                      developed to make collecting large datasets for RL feasible,
                      and to enable a multitude of novel gradient-based
                      accelerator applications. These contributions lay the
                      groundwork for faster accelerator tuning to better working
                      points, and enable new scientific discoveries.},
      keywords     = {reinforcement learning (Other) / differentiable simulation
                      (Other) / particle accelerators (Other) / Natural Sciences
                      and Mathematics::539: Matter; Molecular Physics; Atomic and
                      Nuclear physics; Radiation; Quantum Physics (Other) /
                      Computer Science, Information and General Works::006:
                      Special computer methods::006.3: Artificial
                      Intelligence::006.31: Machine Learning (Other) /
                      Technology::681: Precision Instruments and Other
                      Devices::681.2: Testing, Measuring, Sensing Instruments
                      (Other)},
      cin          = {MSK},
      ddc          = {539},
      cid          = {I:(DE-H253)MSK-20120731},
      pnm          = {621 - Accelerator Research and Development (POF4-621) /
                      InternLabs-0011 - HIR3X - Helmholtz International Laboratory
                      on Reliability, Repetition, Results at the most advanced
                      X-ray Sources $(2020_InternLabs-0011)$},
      pid          = {G:(DE-HGF)POF4-621 / $G:(DE-HGF)2020_InternLabs-0011$},
      experiment   = {EXP:(DE-H253)ARES-20200101 /
                      EXP:(DE-H253)XFEL(machine)-20150101},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      doi          = {10.15480/882.16060},
      url          = {https://bib-pubdb1.desy.de/record/646123},
}