31 citations · 35 across the 5 of their papers we have counts for
Showing 2024Show all
2 papers · 1 filter
physics.acc-ph2024
Microsecond-Latency Feedback at a Particle Accelerator by Online Reinforcement Learning on Hardware
Luca Scomparin, Michele Caselle, Andrea Santamaria Garcia +11
The commissioning and operation of future large-scale scientific experiments will challenge current tuning and control methods. Reinforcement learning (RL) algorithms are a promisi…
physics.acc-ph2024★ 31 cited
Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations
Jan Kaiser, Chenran Xu, Annika Eichler +1
Machine learning has emerged as a powerful solution to the modern challenges in accelerator physics. However, the limited availability of beam time, the computational cost of simul…