WEXBB —  Wednesday Parallel Session 2   (04-Sep-19   08:30—10:00)
Chair: A.R. Gold, SLAC, Menlo Park, California, USA
Paper Title Page
WEXBB1 Adaptive Machine Learning and Automatic Tuning of Intense Electron Bunches in Particle Accelerators 609
  • A. Scheinker
    LANL, Los Alamos, New Mexico, USA
  Machine learning and in particular neural networks, have been around for a very long time. In recent years, thanks to growth in computing power, neural networks have reshaped many fields of research, including self driving cars, computers playing complex video games, image identification, and even particle accelerators. In this tutorial, I will first present an introduction to machine learning for beginners and will also touch on a few aspects of adaptive control theory. I will then introduce some problems in particle accelerators and present how they have been approached utilizing machine learning techniques as well as adaptive machine learning approaches, for automatically tuning extremely short and high intensity electron bunches in free electron lasers.  
slides icon Slides WEXBB1 [58.913 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-NAPAC2019-WEXBB1  
About • paper received ※ 28 August 2019       paper accepted ※ 06 September 2019       issue date ※ 08 October 2019  
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