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@INPROCEEDINGS{Niknejadi:607500,
      author       = {Niknejadi, Pardis and Ackermann, Sven and Ferrari, Eugenio
                      and Lang, Tino and Paraskaki, Georgia and Schaper, Lucas and
                      Schreiber, Siegfried and Vogt, Mathias and Zemella, Johann
                      and Asatrian, Margarit and Hillert, Wolfgang and Pannek,
                      Fabian and Samoilenko, Dmitrii},
      title        = {{I}ntegrating sustainable computational strategies in light
                      source accelerator upgrades},
      address      = {Geneva, Switzerland},
      publisher    = {JACoW Publishing},
      reportid     = {PUBDB-2024-01898},
      isbn         = {978-3-95450-247-9},
      pages        = {343 - 346},
      year         = {2024},
      note         = {JaCOW does not allow addresses with zip code.},
      comment      = {[Ebook] 15th International Particle Accelerator Conference,
                      Nashville, Tennessee : May 19-24, 2024, Nashville,
                      Tennessee, USA : proceedings / Pilat, Fulvia ; Andrian, Ivan
                      , [Geneva] : JACoW Publishing, [2024],},
      booktitle     = {[Ebook] 15th International Particle
                       Accelerator Conference, Nashville,
                       Tennessee : May 19-24, 2024, Nashville,
                       Tennessee, USA : proceedings / Pilat,
                       Fulvia ; Andrian, Ivan , [Geneva] :
                       JACoW Publishing, [2024],},
      abstract     = {The operation of light source accelerators is a complex
                      process that involves a combination of empirical and
                      theoret- ical physics, simulations, and data-intensive
                      methodologies. For example, the FLASH1 beamline at DESY is
                      upgrading to an external seeding FEL light source. We
                      utilize special di- agnostics, machine learning algorithms,
                      and comprehensive simulations to achieve this. To optimize
                      resources, we con- stantly look to improve our approach,
                      allowing us to robustly control the accelerator and meet the
                      desired stability of our users. Machine learning and
                      GPU-based algorithms have be- come crucial, enabling us to
                      employ advanced optimization techniques and possibly
                      Artificial Intelligence. However, in many cases, it is
                      imperative to establish a robust mechanism for simulations
                      involving large particle numbers to ensure that future
                      upgrades and experiments can effectively and sustainably
                      leverage these computational strategies.},
      month         = {May},
      date          = {2024-05-20},
      organization  = {15th International Particle
                       Accelerator Conference, Nashville, TN
                       (USA), 20 May 2024 - 24 May 2024},
      keywords     = {Accelerator Physics (Other) /
                      mc2-photon-sources-and-electron-accelerators - MC2: Photon
                      Sources and Electron Accelerators (Other) / MC2.A06 -
                      MC2.A06 Free Electron Lasers (Other)},
      cin          = {MPY / MFL / FS-LA / UHH},
      cid          = {I:(DE-H253)MPY-20120731 / I:(DE-H253)MFL-20120731 /
                      I:(DE-H253)FS-LA-20130416 / I:(DE-H253)UHH-20231115},
      pnm          = {621 - Accelerator Research and Development (POF4-621)},
      pid          = {G:(DE-HGF)POF4-621},
      experiment   = {EXP:(DE-H253)FLASH2020p-20221201},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.18429/JACoW-IPAC2024-MOPG18},
      url          = {https://bib-pubdb1.desy.de/record/607500},
}