Machine Learning for optimization of design and automated system control of particle accelerator


To get in touch with the accelerator team, please contact Ilya Agapov or Annika Eichler

Team members

  1. Ilya Agapov
  2. Lynda Boukela
  3. Annika Eichler
  4. Christian Grech
  5. Arne Grünhagen
  6. Nur Zulaiha Jomhari
  7. Jan Kaiser
  8. Raimund Kammering
  9. Maximilian Schütte
  10. Oliver Stein
  11. Sergey Tomin
  12. Bianca Veglia

Related projects

Recent publications

  1. Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning: Jan Kaiser et al., arXiv, doi: 10.48550/arXiv.2306.03739
  2. Predictive Maintenance for the Optical Synchronization System of the European XFEL: A Systematic Literature Survey: Arne Grünhagen et al., BTW2023 - Datenbanksysteme für Business, Technologie und Web, doi: 10.18420/BTW2023-70
  3. Anomaly detection at the European X-ray Free Electron Laser using a parity-space-based method: Annika Eichler et al., Physical Review Accelerators and Beams, doi: 10.1103/PhysRevAccelBeams.26.012801
  4. Application of Machine Learning in Longitudinal Phase Space Prediction at the European XFEL: Zihan Zhu et al., Proceedings of the 40th International Free Electron Laser Conference (FEL2022), doi:
  5. Convex Synthesis of Robust Distributed Controllers for the Optical Synchronization System at European XFEL: Maximilian Schütte et al., 2022 IEEE Conference on Control Technology and Applications (CCTA), doi: 10.1109/CCTA49430.2022.9966114