3 papers
physics.acc-ph2026
The Memory Scaling of Reverse-Mode Differentiation in Particle Accelerator Simulations with Space Charge
Arjun Dhamrait, Edoardo Zoni, Axel Huebl +7
The recent development of differentiable simulation codes for particle accelerators has enabled gradient-based workflows that promise finer control and more realistic modeling of a…
physics.acc-ph2024
Towards Unlocking Insights from Logbooks Using AI
Antonin Sulc, Alex Bien, Annika Eichler +15
Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of lo…
cs.CL2024
Large Language Models for Human-Machine Collaborative Particle Accelerator Tuning through Natural Language
Jan Kaiser, Annika Eichler, Anne Lauscher
Autonomous tuning of particle accelerators is an active and challenging field of research with the goal of enabling novel accelerator technologies cutting-edge high-impact applicat…