Orateur
Description
Accurate beam dynamics simulations including collective effects are essential for the design of future particle accelerators, but high-fidelity macroparticle tracking can become computationally expensive, especially when large parameter scans are required. We investigate machine learning surrogate models for the evolution of beam distributions in the FCC-ee High-Energy Booster.
Rather than predicting the trajectories of individual macroparticles, the approach learns the evolution of the beam distribution itself. Different architectures are explored, including Fourier Neural Operators, conditional variational autoencoders, Transformers, and graph-based neural operators, using data generated from accelerator tracking simulations. Particular attention is given to preserving the physical structure of the accelerator and to learning local interactions along the machine lattice. The results illustrate how structure-aware machine learning can provide fast approximations of beam distribution evolution. This suggests a promising route toward more efficient computational studies of collective effects in beam dynamics.