Machine learning and applications in ultrafast photonics

Goëry Genty, Lauri Salmela, John M. Dudley, Daniel Brunner, Alexey Kokhanovskiy, Sergei Kobtsev, Sergei K. Turitsyn

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)

Abstract

Recent years have seen the rapid growth and development of the field of smart photonics, where machine-learning algorithms are being matched to optical systems to add new functionalities and to enhance performance. An area where machine learning shows particular potential to accelerate technology is the field of ultrafast photonics — the generation and characterization of light pulses, the study of light–matter interactions on short timescales, and high-speed optical measurements. Our aim here is to highlight a number of specific areas where the promise of machine learning in ultrafast photonics has already been realized, including the design and operation of pulsed lasers, and the characterization and control of ultrafast propagation dynamics. We also consider challenges and future areas of research.

Original languageEnglish
Pages (from-to)91-101
Number of pages11
JournalNature Photonics
Volume15
Issue number2
Early online date30 Nov 2020
DOIs
Publication statusPublished - Feb 2021

Keywords

  • LOCKED FIBER LASER
  • GENETIC ALGORITHM
  • PHASE RETRIEVAL
  • NEURAL-NETWORKS
  • ADAPTIVE OPTICS
  • OPTIMIZATION
  • PULSES
  • DESIGN
  • FILTERS
  • SYSTEM

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