Author

F. Velotti

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Preprint Aug 2026

Particle tracking with physics-informed deep learning methods

Simulating the motion of charged particles in electromagnetic fields is essential for designing and optimising particle accelerators. Conventional tools rely on symplectic integration schemes, which provide high accuracy but are computationally expensive. As a consequence, optimisation in moderate to high-dimensional p...

Matthias Remta, Anja Beck, Shanthalakshmi Kilambi et al. · 1 citation

Preprint Aug 2026

Flow-based surrogate models for particle tracking

To capture distribution-dependent dynamics that vanilla CFM cannot represent, the model is extended with cross-attention over the initial particle ensemble (Cross-Attention-CFM) and it is demonstrated that this extension recovers performance on a space-charge benchmark in the PS, where vanilla CFM degrades.

Matthias Remta, Y. Dutheil, F. Velotti · 0 citations