IPAC2021 - List of Authors (Burger, M.)

Paper	Title	Page
THPAB349	Feed-Forward Neural Network Based Modelling of an Ultrafast Laser for Enhanced Control	4478
	A. Aslam, M. Martínez-Ramón, S.D. Scott UNM-ECE, Albuquerque, USA S. Biedron Argonne National Laboratory, Office of Naval Research Project, Argonne, Illinois, USA S. Biedron Element Aero, Chicago, USA S. Biedron UNM-ME, Albuquerque, New Mexico, USA M. Burger, J. Murphy NERS-UM, Ann Arbor, Michigan, USA K.M. Krushelnick, J. Nees, A.G.R. Thomas University of Michigan, Ann Arbor, Michigan, USA Y. Ma IHEP, Beijing, People’s Republic of China Y. Ma Michigan University, Ann Arbor, Michigan, USA
	Funding: Acknowledgements: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under award number DE-SC0019468. 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.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB349
About •	paper received ※ 20 May 2021 paper accepted ※ 02 July 2021 issue date ※ 17 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Feed-Forward Neural Network Based Modelling of an Ultrafast Laser for Enhanced Control

4478

A. Aslam, M. Martínez-Ramón, S.D. Scott
UNM-ECE, Albuquerque, USA
S. Biedron
Argonne National Laboratory, Office of Naval Research Project, Argonne, Illinois, USA
S. Biedron
Element Aero, Chicago, USA
S. Biedron
UNM-ME, Albuquerque, New Mexico, USA
M. Burger, J. Murphy
NERS-UM, Ann Arbor, Michigan, USA
K.M. Krushelnick, J. Nees, A.G.R. Thomas
University of Michigan, Ann Arbor, Michigan, USA
Y. Ma
IHEP, Beijing, People’s Republic of China
Y. Ma
Michigan University, Ann Arbor, Michigan, USA

Funding: Acknowledgements: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under award number DE-SC0019468.
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.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB349

About •

paper received ※ 20 May 2021 paper accepted ※ 02 July 2021 issue date ※ 17 August 2021

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)