Applications of Machine Learning to Analysis and Computation for Particle Accelerators

Date and Time: 
Tuesday April 9th 2019
CG Auditorium
Jonathan Edelen
This talk provides an overview of several ways in which machine learning can be applied to improve computational capabilities for modeling particle accelerators. We begin with an overview of the machine learning methods of interest. Then we provide some background on particle accelerators and how these methods could improve our modeling capabilities. We conclude by showing recent results for the electron injector at the Fermilab Accelerator Science and Technology Facility demonstrating these capabilities.
Speaker Description: 
Jonathan Edelen is an accelerator physicist with a broad range of experience across the field. Currently, He is working on the development of symplectic space charge algorithms, novel symplectic algorithms for phase space deposition, machine learning for modeling and control of particle accelerators, and the application of nonlinear optimization methods to accelerator technology such as thermionic energy converters.
PDF icon Edelen_SEA2019.pdf15.81 MB

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