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TY - CONF AU - Aslam, A. AU - Biedron, S. AU - Burger, M. AU - Krushelnick, K.M. AU - Ma, Y. AU - Martínez-Ramón, M. AU - Murphy, J. AU - Nees, J. AU - Scott, S.D. AU - Thomas, A.G.R. ED - Liu, Lin ED - Byrd, John M. ED - Neuenschwander, Regis T. ED - Picoreti, Renan ED - Schaa, Volker R. W. TI - Feed-Forward Neural Network Based Modelling of an Ultrafast Laser for Enhanced Control J2 - Proc. of IPAC2021, Campinas, SP, Brazil, 24-28 May 2021 CY - Campinas, SP, Brazil T2 - International Particle Accelerator Conference T3 - 12 LA - english AB - The applications of machine learning in today’s world encompass all fields of life and physical sciences. In this paper, we implement a machine learning based algorithm in the context of laser physics and particle accelerators. Specifically, a neural network-based optimisation algorithm has been developed that offers enhanced control over an ultrafast femtosecond laser in comparison to the traditional Proportional Integral and derivative (PID) controls. This research opens a new potential of utilising machine learning and even deep learning techniques to improve the performance of several different lasers and accelerators systems. PB - JACoW Publishing CP - Geneva, Switzerland SP - 4478 EP - 4480 KW - laser KW - network KW - controls KW - electron KW - cathode DA - 2021/08 PY - 2021 SN - 2673-5490 SN - 978-3-95450-214-1 DO - doi:10.18429/JACoW-IPAC2021-THPAB349 UR - https://jacow.org/ipac2021/papers/thpab349.pdf ER -