Journals of Accelerator Conferences Website (JACoW)

JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelator conferences held around the world by an international collaboration of editors.

ris citation export for WEPS066: Multi-objective optimization of ring cyclotron RF cavity using neural network ensembles with uncertainty quantification

TY - CONF
AU - Shali, A.
AU - Kanda, H.
AU - Fukuda, M.
AU - Yorita, T.
ED - 
TI - Multi-objective optimization of ring cyclotron RF cavity using neural network ensembles with uncertainty quantification
J2 - Proc. of ipac2025, Taipei, Taiwan, 01-06 June 2025
CY - Taipei, Taiwan
T2 - IPAC'25 - 16th International Particle Accelerator Conference
T3 - 16
LA - English
AB - 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.
PB - JACoW Publishing
CP - Geneva, Switzerland
SP - 2342
EP - 2345
KW - 
DA - 2025/06
PY - 2025
SN - 2673-5490
DO - 10.18429/JACoW-IPAC25-WEPS066
UR - https://indico.jacow.org/event/81/contributions/8517
ER -