2023/06/06 by Jan Kaiser, Chenran Xu, Kaiser, Jan +21
Engineering · #Accelerator Physics (physics.acc-ph) #Artificial Intelligence (cs.AI) #Experimental Learning in Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Reservoir Engineering and Simulation Methods
paper · pdf · doi:10.48550/arxiv.2306.03739
openalex publication_date 2023/06/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Online tuning of real-world plants 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, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study using a routine task in a real particle accelerator as an example, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study's results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering advancements.