Data-Driven Optimisation of Superconducting Magnets at CEA Paris-Saclay

TL;DR

This paper introduces an AI-based platform for optimizing superconducting magnets, leveraging machine learning and advanced data management to improve design processes and address complex physical phenomena.

Contribution

It presents a novel AI-driven platform and showcases applications like multiphysics optimization, topology optimization, and anomaly detection in superconducting magnet design.

Findings

Enhanced multiphysics optimization using active learning.

Successful topology optimization of magnet components.

Effective anomaly detection in quench events.

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.