1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Machine learning-based symplectic model for space-charge effect simulation

WEPS011
4 Jun 2025, 16:00
2h
Exhibiton Hall A _Salmon (TWTC)

Exhibiton Hall A _Salmon

TWTC

Poster Presentation MC5.D07 High Intensity Circular Machines Space Charge, Halos Wednesday Poster Session

Speaker

Jinyu Wan (Facility for Rare Isotope Beams)

Description

Symplectic simulation of space-charge effects is important for high-intensity particle accelerators. In this work, we propose to use a generative model to efficiently simulate space-charge effects. The one-step symplectic transverse transfer map of the particles is obtained by differentiating the predicted space-charge Hamiltonian. This model effectively preserves the phase-space structure and reduces non-physical effects in long-term simulations by ensuring symplecticity in the calculation.

Funding Agency

This work is supported by DOE office of science, with award number DE-SC0024170.

Region represented America
Paper preparation format LaTeX

Author

Jinyu Wan (Facility for Rare Isotope Beams)

Co-authors

Yue Hao (Facility for Rare Isotope Beams) Ji Qiang (Lawrence Berkeley National Laboratory)

Presentation materials

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