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| URL | https://doi.org/10.18429/JACoW-IPAC2025-WEPS066 |
|---|---|
| Title | Multi-objective optimization of ring cyclotron RF cavity using neural network ensembles with uncertainty quantification |
| Authors |
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| Abstract | This study presents a multi-objective optimization scheme for ring cyclotron RF cavities, leveraging a neural network ensemble surrogate model. The cavity geometry is parameterized using Non-Uniform Rational B-Splines (NURBS), with control points and weights as design parameters. To reduce the computational cost of direct eigenmode simulations, an ensemble of neural networks trained using Ansys HFSS results is used to approximate performance metrics efficiently. The surrogate model also quantifies uncertainty, enabling Monte Carlo error propagation to account for potential manufacturing deviations. A multi-objective genetic algorithm (MOGA) explores the design space, using the surrogate model for efficient evaluations. The neural network ensemble are periodically retrained through HFSS simulations, iteratively improving the accuracy of surrogate model. This approach gives a robust and reliable RF cavity design optimization scheme. |
| Paper | download: WEPS066.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 | 27 May 2025 |
| Revised | 01 June 2025 |
| Accepted | 01 June 2025 |
| Issued | 05 November 2025 |
| DOI | 10.18429/JACoW-IPAC2025-WEPS066 |
| Pages | 2381-2384 |
| 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. |