Speaker
Description
Beam dynamics under collective effects such as space charge remains a computationally expensive challenge. We present a latent space surrogate model for collective beam dynamics that significantly accelerates these simulations. The method uses a variational autoencoder to compress 6D particle distributions into a low-dimensional latent space. A learned latent-space dynamical model then predicts beam evolution directly in the latent space, bypassing expensive space charge solvers. Using simulated data from a space charge dominated lattice, this approach reproduces beam envelope evolution with good agreement to particle-in-cell codes while offering substantial speedups. This framework provides a flexible path towards fast beam prediction for online accelerator modeling.
Funding Agency
This work was supported by DOE Tigner Traineeship in Accelerator Science and DOE grant DE-SC0025351.
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