SinoXivData-Driven Optimisation of Superconducting Magnets at CEA Paris-Saclay
Abstract
Superconducting magnets for particle accelerators are particularly challenging to design because they involve a large number of coupled physical phenomena and the management of complex datasets. Artificial Intelligence (AI), including machine learning and advanced optimisation techniques, offers promising approaches to address these challenges and accelerate the design process. This paper presents a new AI-based optimisation and data management platform, and highlights several ongoing applications of AI methods carried out at CEA Paris-Saclay, including multiphysics optimisation using active learning, topology optimisation, holistic modelling of an Electron Cyclotron Resonance (ERC) ion source, and anomaly detection in quench events.
Keywords
Citation Information
@article{damienfgminenna2026,
title={Data-Driven Optimisation of Superconducting Magnets at CEA Paris-Saclay},
author={Damien F. G. Minenna and Guillaume Dilasser and Robin Penavaire and Valerio Calvelli and Thibault de Chabannes and Thibault Lecrevisse and Thomas Achard and Jason Le Coz and Christophe Berriaud and Benoît Bolzon and Antomne Caunes and Phillipe Fazilleau and Hélène Felice and Clément Genot and Antoine Guinet and Nikola Jerance and François-Paul Juster and Thibaut Lemercier and Gilles Lenoir and Clément Lorin and Yann Perron and Camille Pucheu-Plante and Étienne Rochepault and Damien Simon and Francesco Stacchi and Michel Segreti and Vincent Trauchessec and Olivier Tuske and Hajar Zgour},
journal={EPJ Research Infrastructures},
year={2026},
doi={https://doi.org/10.21203/rs.3.rs-9367074/v1}
}
