Machine learning and applications in ultrafast photonics


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.

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Fig. 1: Overview of the main machine-learning concepts and implementations that can be used in ultrafast photonics.
Fig. 2: Illustration of machine-learning strategies for optimization and self-tuning of ultrafast fibre lasers using control of intracavity elements via a feedback loop and control algorithm.
Fig. 3: Machine-learning applications in ultrafast photonics.


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G.G. acknowledges the Academy of Finland (318082, 333949, Flagship PREIN 320165). L.S. acknowledges the Faculty of Engineering and Natural Sciences graduate school of Tampere University. J.M.D. and D.B. were supported by the EUR EIPHI and I-SITE BFC projects (contracts ANR-17-EURE-0002 and ANR-15-IDEX- 0003). D.B. also acknowledges funding from the Volkswagen Foundation and from the French Agence Nationale de la Recherche (ANR-19-CE24-0006-02). The work of S.K.T. and A.K. was supported by the Russian Science Foundation (grant number 17-72-30006). S.K.T. acknowledges the support of the EPSRC project TRANSNET. The work of S.K. was supported by the Russian Foundation for Basic Research grant number 18-29-20025.

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Genty, G., Salmela, L., Dudley, J.M. et al. Machine learning and applications in ultrafast photonics. Nat. Photonics 15, 91–101 (2021).

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