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RIS citation export for SUPAG08: Machine Learning for X-Ray Free-Electron Lasers

TY  - UNPB
AU  - Ratner, D.F.
ED  - Schaa, Volker RW
ED  - Makino, Kyoko
ED  - Snopok, Pavel
ED  - Berz, Martin
TI  - Machine Learning for X-Ray Free-Electron Lasers
J2  - Proc. of ICAP2018, Key West, FL, USA, 20-24 October 2018
CY  - Key West, FL, USA
T2  - International Computational Accelerator Physics Conference
T3  - 13
LA  - english
AB  - X-ray Free Electron Lasers (XFELs) are among the most complex accelerator projects in the world today. With large parameter spaces, sensitive dependence on beam quality, huge data rates, and challenging machine protection, there are expanding opportunities to apply machine learning (ML) to XFEL operation. In this talk I will summarize some promising ML methods for XFELs, and highlight recent examples of successful applications.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
ER  -