5 citations · 8 across the 6 of their papers we have counts for
6 papers
Beyond PID Controllers: PPO with Neuralized PID Policy for Proton Beam Intensity Control in Mu2e
Chenwei Xu, Jerry Yao-Chieh Hu, Aakaash Narayanan +21
We introduce a novel Proximal Policy Optimization (PPO) algorithm aimed at addressing the challenge of maintaining a uniform proton beam intensity delivery in the Muon to Electron…
ML-based Real-Time Control at the Edge: An Approach Using hls4ml
R. Shi, S. Ogrenci, J. M. Arnold +16
This study focuses on implementing a real-time control system for a particle accelerator facility that performs high energy physics experiments. A critical operating parameter in t…
Synchronous High-frequency Distributed Readout For Edge Processing At The Fermilab Main Injector And Recycler
J. R. Berlioz, M. R. Austin, J. M. Arnold +17
The Main Injector (MI) was commissioned using data acquisition systems developed for the Fermilab Main Ring in the 1980s. New VME-based instrumentation was commissioned in 2006 for…
Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster
D. Kafkes, M. Schram
We describe the offline machine learning (ML) development for an effort to precisely regulate the Gradient Magnet Power Supply (GMPS) at the Fermilab Booster accelerator complex vi…
Accelerator Real-time Edge AI for Distributed Systems (READS) Proposal
K. Seiya, K. J. Hazelwood, M. A. Ibrahim +7
Our objective will be to integrate ML into Fermilab accelerator operations and furthermore provide an accessible framework which can also be used by a broad range of other accelera…
Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
Jason St. John, Christian Herwig, Diana Kafkes +11
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…