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| URL | https://doi.org/10.18429/JACoW-IPAC2025-WEPS011 |
|---|---|
| Title | Machine learning-based symplectic model for space-charge effect simulation |
| Authors |
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| 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. |
| Paper | download: WEPS011.pdf |
| Cite | BibTeX, LaTeX, Text/Word, RIS, EndNote |
| Conference | 16th International Particle Accelerator Conference |
| Series | |
| Location | Taipei, Taiwan |
| Date | 01-06 Jun 2025 |
| Publisher | JACoW Publishing, Geneva, Switzerland |
| Editorial Board | * Ming-Chyuan Lin - National Synchrotron Radiation Research Center * Yoichi Sato - KEK * Jui-Che Huang - National Synchrotron Radiation Research Center * David Button - Australian Nuclear Science and Technology Organisation * Ping-Shun Chuang - National Synchrotron Radiation Research Center |
| Online ISBN | 978-3-95450-248-6 |
| Online ISSN | 2673-5490 |
| Received | 16 May 2025 |
| Revised | 01 June 2025 |
| Accepted | 02 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-WEPS011 |
| Pages | 2264-2266 |
| Copyright | Published by JACoW Publishing under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |