14 citations · 20 across the 6 of their papers we have counts for
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cs.LG2020★ 5 cited
Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL
Simon Hirlaender, Niky Bruchon
Reinforcement learning holds tremendous promise in accelerator controls. The primary goal of this paper is to show how this approach can be utilised on an operational level on acce…
physics.acc-ph2020
Online Multi-Objective Particle Accelerator Optimization of the AWAKE Electron Beam Line for Simultaneous Emittance and Orbit Control
Alexander Scheinker, Simon Hirlaende, Francesco Maria Velotti +4
Multi-objective optimization is important for particle accelerators where various competing objectives must be satisfied routinely such as, for example, transverse emittance vs bun…