IPAC'24 - 15th International Particle Accelerator Conference

Advancing non-linear space charge simulations: neural networks and analytical approaches

SUPG004

19 May 2024, 14:00

4h

Bluegrass (MCC Exhibit Hall A)

Student Poster Presentation MC5.D02 Nonlinear Single Particle Dynamics Resonances, Tracking, Higher Order, Dynamic Aperture, Code Developments Student Poster Session

Speaker

Isabella Vojskovic (European Spallation Source ERIC)

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 electric fields. The model demonstrates robust performance, achieving a root mean squared error (RMSE) of $0.5\%$ within just a few minutes of training. Furthermore, this paper explores the feasibility of replacing traditional ellipsoidal methods with Gaussian envelope models for improved non-linear space-charge calculations. Our findings indicate that these advancements could provide a more efficient alternative to numerical space-charge methods in beam dynamics simulations.