Machine learning and applications in ultrafast photonics › Обзор исследований

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Machine learning and applications in ultrafast photonics. / Genty, Goëry; Salmela, Lauri; Dudley, John M. и др.

в: Nature Photonics, Том 15, № 2, 02.2021, стр. 91-101.

Результаты исследований: Научные публикации в периодических изданиях › обзорная статья › Рецензирование

Harvard

Genty, G, Salmela, L, Dudley, JM, Brunner, D, Kokhanovskiy, A , Kobtsev, S & Turitsyn, SK 2021, 'Machine learning and applications in ultrafast photonics', Nature Photonics, Том. 15, № 2, стр. 91-101. https://doi.org/10.1038/s41566-020-00716-4

APA

Genty, G., Salmela, L., Dudley, J. M., Brunner, D., Kokhanovskiy, A., Kobtsev, S., & Turitsyn, S. K. (2021). Machine learning and applications in ultrafast photonics. Nature Photonics, 15(2), 91-101. https://doi.org/10.1038/s41566-020-00716-4

Vancouver

Genty G, Salmela L, Dudley JM, Brunner D, Kokhanovskiy A , Kobtsev S и др. Machine learning and applications in ultrafast photonics. Nature Photonics. 2021 февр.;15(2):91-101. Epub 2020 нояб. 30. doi: 10.1038/s41566-020-00716-4

Author

Genty, Goëry ; Salmela, Lauri ; Dudley, John M. и др. / Machine learning and applications in ultrafast photonics. в: Nature Photonics. 2021 ; Том 15, № 2. стр. 91-101.

BibTeX

@article{faf6a81d525945c8bcc4d2afc91c3d5b,

title = "Machine learning and applications in ultrafast photonics",

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.",

keywords = "LOCKED FIBER LASER, GENETIC ALGORITHM, PHASE RETRIEVAL, NEURAL-NETWORKS, ADAPTIVE OPTICS, OPTIMIZATION, PULSES, DESIGN, FILTERS, SYSTEM",

author = "Go{\"e}ry Genty and Lauri Salmela and Dudley, {John M.} and Daniel Brunner and Alexey Kokhanovskiy and Sergei Kobtsev and Turitsyn, {Sergei K.}",

year = "2021",

month = feb,

doi = "10.1038/s41566-020-00716-4",

language = "English",

volume = "15",

pages = "91--101",

journal = "Nature Photonics",

issn = "1749-4885",

publisher = "Nature Publishing Group",

number = "2",

}

RIS

TY - JOUR

T1 - Machine learning and applications in ultrafast photonics

AU - Genty, Goëry

AU - Salmela, Lauri

AU - Dudley, John M.

AU - Brunner, Daniel

AU - Kokhanovskiy, Alexey

AU - Kobtsev, Sergei

AU - Turitsyn, Sergei K.

PY - 2021/2

Y1 - 2021/2

N2 - 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.

AB - 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.

KW - LOCKED FIBER LASER

KW - GENETIC ALGORITHM

KW - PHASE RETRIEVAL

KW - NEURAL-NETWORKS

KW - ADAPTIVE OPTICS

KW - OPTIMIZATION

KW - PULSES

KW - DESIGN

KW - FILTERS

KW - SYSTEM

UR - http://www.scopus.com/inward/record.url?scp=85096938518&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/567ca54d-727b-3ae9-bb28-9ec7649a130c/

U2 - 10.1038/s41566-020-00716-4

DO - 10.1038/s41566-020-00716-4

M3 - Review article

AN - SCOPUS:85096938518

VL - 15

SP - 91

EP - 101

JO - Nature Photonics

JF - Nature Photonics

SN - 1749-4885

IS - 2

ER -

ID: 26205838