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@inproceedings{aslam:ipac2021-thpab349,
author = {A. Aslam and S. Biedron and M. Burger and K.M. Krushelnick and Y. Ma and M. Martínez-Ramón and J. Murphy and J. Nees and S.D. Scott and A.G.R. Thomas},
% author = {A. Aslam and S. Biedron and M. Burger and K.M. Krushelnick and Y. Ma and M. Martínez-Ramón and others},
% author = {A. Aslam and others},
title = {{Feed-Forward Neural Network Based Modelling of an Ultrafast Laser for Enhanced Control}},
booktitle = {Proc. IPAC'21},
pages = {4478--4480},
eid = {THPAB349},
language = {english},
keywords = {laser, network, controls, electron, cathode},
venue = {Campinas, SP, Brazil},
series = {International Particle Accelerator Conference},
number = {12},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {08},
year = {2021},
issn = {2673-5490},
isbn = {978-3-95450-214-1},
doi = {10.18429/JACoW-IPAC2021-THPAB349},
url = {https://jacow.org/ipac2021/papers/thpab349.pdf},
note = {https://doi.org/10.18429/JACoW-IPAC2021-THPAB349},
abstract = {{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.}},
}