Machine Learning Techniques for Uncertainty Estimation in Dynamic Aperture Prediction

Machine Learning Techniques for Uncertainty Estimation in Dynamic Aperture Prediction

The dynamic aperture is an essential concept in circular particle accelerators, providing the extent of the phase space region where particle motion remains stable over multiple turns. The accurate prediction of the dynamic aperture is key to optimising performance in accelerators such as the CERN L...

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Bibliographic Details
Main Authors: Carlo Emilio Montanari, Robert B. Appleby, Davide Di Croce, Massimo Giovannozzi, Tatiana Pieloni, Stefano Redaelli, Frederik F. Van der Veken
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Computers
Subjects:
surrogate modelling
uncertainty quantification
Monte Carlo dropout
bootstrap aggregation
dynamic aperture
accelerator modelling
Online Access:https://www.mdpi.com/2073-431X/14/7/287
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