Author: John, Jason St.; Herwig, Christian; Kafkes, Diana; Mitrevski, Jovan; Pellico, William A.; Perdue, Gabriel N.; Quintero-Parra, Andres; Schupbach, Brian A.; Seiya, Kiyomi; Tran, Nhan; Schram, Malachi; Duarte, Javier M.; Huang, Yunzhi; Laboratory, Rachael Keller Fermi National Accelerator; Laboratory, Thomas Jefferson National Accelerator; Diego, University of California San; Laboratory, Pacific Northwest National; University, Columbia
Title: Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster Cord-id: c9aiu5uc Document date: 2020_11_14
ID: c9aiu5uc
Snippet: We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning. We demonstrate preliminary results by training a surrogate machine-learning model on real accelerator data to emulate the Booster environment, and using this surrogate model in turn to train the neural network for its regulation task. We additionally show how the neural networks to be deployed for control purposes ma
Document: We describe a method for precisely regulating the gradient magnet power supply at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning. We demonstrate preliminary results by training a surrogate machine-learning model on real accelerator data to emulate the Booster environment, and using this surrogate model in turn to train the neural network for its regulation task. We additionally show how the neural networks to be deployed for control purposes may be compiled to execute on field-programmable gate arrays. This capability is important for operational stability in complicated environments such as an accelerator facility.
Search related documents:
Co phrase search for related documents- absolute pearson correlation and machine learning: 1, 2
Co phrase search for related documents, hyperlinks ordered by date