Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning

Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning

Abstract Online tuning of particle accelerators is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods like Bayesian optimisation (BO) hold great promise...

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Bibliographic Details
Main Authors: Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia, Oliver Stein, Erik Bründermann, Willi Kuropka, Hannes Dinter, Frank Mayet, Thomas Vinatier, Florian Burkart, Holger Schlarb
Format: Article
Language:English
Published: Nature Portfolio 2024-07-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-66263-y

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https://doi.org/10.1038/s41598-024-66263-y

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