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proceedings of accelerator conferences held around the world
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| URL | https://doi.org/10.18429/JACoW-IPAC2025-WEPM073 |
|---|---|
| Title | Electromagnetic modeling of cryogenic vacuum chambers using a hybrid neural network-boundary integral equation approach |
| Authors |
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| Abstract | Recently, artificial intelligence and machine learning are actively discussed in the particle accelerator community. The physics-informed neural network (PINN) method, which is a powerful approach for solving differential equations with deep neural networks (DNN), has been successfully applied to the calculation of electromagnetic fields and beam coupling impedances in particle accelerators. In this work, a hybrid PINN method combined with the boundary integral equation (BIE) method is developed to calculate the fields and impedances in accelerator vacuum chambers at cryogenic temperature. The surface impedance boundary condition for the anomalous skin effect is included to model the electromagnetic characteristics of chamber wall surfaces. Transfer learning can accelerate training processes for DNN parameters in a wide frequency band. The hybrid PINN-BIE approach is verified through applications to various chamber cross sections. |
| Paper | download: WEPM073.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 | 26 May 2025 |
| Revised | 30 May 2025 |
| Accepted | 02 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-WEPM073 |
| Pages | 2133-2136 |
| 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. |