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Surrogate Modelling of the FLUTE Low-Energy Section

Xu, C. ORCID iD icon 1; Bründermann, E. ORCID iD icon 1; Müller, A-S. ORCID iD icon 1; Santamaria Garcia, Andrea ORCID iD icon 1; Schäfer, J. 1
1 Institut für Beschleunigerphysik und Technologie (IBPT), Karlsruher Institut für Technologie (KIT)


Numerical beam dynamics simulations are essential tools in the study and design of particle accelerators, but they can be prohibitively slow for online prediction during operation or for systematic evaluations of new parameter settings. Machine learning-based surrogate models of the accelerator provide much faster predictions of the beam properties and can serve as a virtual diagnostic or to augment data for reinforcement learning training. In this paper, we present the first results on training a surrogate model for the low-energy section at the Ferninfrarot Linac- und Test-Experiment (FLUTE).

Zugehörige Institution(en) am KIT Institut für Beschleunigerphysik und Technologie (IBPT)
Laboratorium für Applikationen der Synchrotronstrahlung (LAS)
Publikationstyp Poster
Publikationsjahr 2022
Sprache Englisch
Identifikator KITopen-ID: 1000149642
HGF-Programm 54.11.11 (POF IV, LK 01) Accelerator Operation, Research and Development
Veranstaltung 13th International Particle Accelerator Conference (IPAC 2022), Bangkok, Thailand, 12.06.2022 – 17.06.2022
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