Speaker
Chenran Xu
(KIT)
Description
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. Ma-
chine learning-based surrogate models of the accelerator pro-
vide much faster predictions of the beam properties and can
serve as a virtual diagnostic or to augment data for reinforce-
ment learning training. In this paper, we present the first re-
sults on training a surrogate model for the low-energy section
at the Ferninfrarot Linac- und Test-Experiment (FLUTE).
Primary author
Chenran Xu
(KIT)
Co-authors
Andrea Santamaria Garcia
(KIT)
Jens Schäfer
(KIT IBPT)
Erik Bruendermann
(KIT)
Anke-Susanne Mueller
(KIT)
