Using machine learning to explore the dynamics of complex laser systems

J. P. Toomey*, A. Reid, L. McCalman, T. Malica, D. M. Kane

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Proof of principle study using a machine learning algorithm to actively sample the operating parameter space of a complex laser system, in real-time, to find the boundaries between differentiated dynamical regions efficiently.

Original languageEnglish
Title of host publicationPhotonics and Fiber Technology 2016 (ACOFT, BGPP, NP)
Subtitle of host publicationproceedings
PublisherOSA - The Optical Society
Pages1-2
Number of pages2
ISBN (Print)9781943580170
DOIs
Publication statusPublished - 29 Aug 2016
EventAustralian Conference on Optical Fibre Technology, ACOFT 2016 - Sydney, Australia
Duration: 5 Sep 20168 Sep 2016

Other

OtherAustralian Conference on Optical Fibre Technology, ACOFT 2016
CountryAustralia
CitySydney
Period5/09/168/09/16

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Toomey, J. P., Reid, A., McCalman, L., Malica, T., & Kane, D. M. (2016). Using machine learning to explore the dynamics of complex laser systems. In Photonics and Fiber Technology 2016 (ACOFT, BGPP, NP): proceedings (pp. 1-2). [JT4A.18-1] OSA - The Optical Society. https://doi.org/10.1364/ACOFT.2016.JT4A.18