Bayesian optimization algorithms for accelerator physics
Accelerator physics relies on numerical algorithms to solve optimization problems in online
accelerator control and tasks such as experimental design and model calibration in …
accelerator control and tasks such as experimental design and model calibration in …
Sample-efficient reinforcement learning for CERN accelerator control
V Kain, S Hirlander, B Goddard, FM Velotti… - … Review Accelerators and …, 2020 - APS
Numerical optimization algorithms are already established tools to increase and stabilize the
performance of particle accelerators. These algorithms have many advantages, are …
performance of particle accelerators. These algorithms have many advantages, are …
Reinforcement learning-trained optimisers and Bayesian optimisation for online particle accelerator tuning
Online tuning of particle accelerators is a complex optimisation problem that continues to
require manual intervention by experienced human operators. Autonomous tuning is a …
require manual intervention by experienced human operators. Autonomous tuning is a …
Learning to do or learning while doing: Reinforcement learning and bayesian optimisation for online continuous tuning
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 …
require manual intervention by experienced human operators. Autonomous tuning is a …
Injection Optimization at Particle Accelerators via Reinforcement Learning: From Simulation to Real-World Application
Optimizing the injection process in particle accelerators is crucial for enhancing beam
quality and operational efficiency. This paper presents a framework for utilizing …
quality and operational efficiency. This paper presents a framework for utilizing …
Large language models for human-machine collaborative particle accelerator tuning through natural language
Autonomous tuning of particle accelerators is an active and challenging field of research
with the goal of enabling novel accelerator technologies cutting-edge high-impact …
with the goal of enabling novel accelerator technologies cutting-edge high-impact …
Accelerated deep reinforcement learning for fast feedback of beam dynamics at KARA
W Wang, M Caselle, T Boltz, E Blomley… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Coherent synchrotron radiation (CSR) is generated when the electron bunch length is in the
order of the magnitude of the wavelength of the emitted radiation. The self-interaction of …
order of the magnitude of the wavelength of the emitted radiation. The self-interaction of …
Orbit correction based on improved reinforcement learning algorithm
Recently, reinforcement learning (RL) algorithms have been applied to a wide range of
control problems in accelerator commissioning. In order to achieve efficient and fast control …
control problems in accelerator commissioning. In order to achieve efficient and fast control …
[PDF][PDF] Micro-bunching control at electron storage rings with reinforcement learning
T Boltz - 2021 - scholar.archive.org
At the time this thesis is written, the world nds itself amidst and partly in the process of
recovering from the COVID-19 pandemic caused by the SARS-Cov-2 virus. One major …
recovering from the COVID-19 pandemic caused by the SARS-Cov-2 virus. One major …
Trend-Based SAC Beam Control Method with Zero-Shot in Superconducting Linear Accelerator
The superconducting linear accelerator is a highly flexiable facility for modern scientific
discoveries, necessitating weekly reconfiguration and tuning. Accordingly, minimizing setup …
discoveries, necessitating weekly reconfiguration and tuning. Accordingly, minimizing setup …