Machine learning-based symplectic model for space-charge effect simulation
Year: 2025
Type: conference-paper
Abstract: 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 in JuTrack, a Julia-based particle tracking code. 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.
Source: JACOW
Authors: Wan,Jinyu, Hao,Yue, Qiang,Ji
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7104258053
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