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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 -