Speaker
Isabella Vojskovic
(Lund University)
Description
This study explores various neural network approaches for simulating beam dynamics, with a particular focus on non-linear space charge effects. We introduce a convolutional encoder-decoder architecture that incorporates skip connections to predict transversal but also coupled 3D electric self-fields. The model demonstrates robust performance, achieving a Mean Absolute Percentage Error (MAPE) of $0.5\%$ within just a few minutes of training. Our findings indicate that these advancements could provide a more efficient alternative to numerical non-linear space-charge methods in beam dynamics simulations, where the speed up is significant.
Primary author
Isabella Vojskovic
(Lund University)
Co-author
Mr
Emanuele Laface
