Machine learning analysis of extreme events in optical fibre modulation instability

Real-time characterisation of nonlinear processes in the time domain is challenging. Here, Närhi et al. show that machine learning techniques can help overcome this limitation and use them to infer time-domain properties of optical fibre modulation instability from spectral intensity measurements.

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Autores principales:	Mikko Närhi, Lauri Salmela, Juha Toivonen, Cyril Billet, John M. Dudley, Goëry Genty
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/2189e5907a1e496d9adc09f2e86e99a5
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spelling	oai:doaj.org-article:2189e5907a1e496d9adc09f2e86e99a52021-12-02T17:33:19ZMachine learning analysis of extreme events in optical fibre modulation instability10.1038/s41467-018-07355-y2041-1723https://doaj.org/article/2189e5907a1e496d9adc09f2e86e99a52018-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-07355-yhttps://doaj.org/toc/2041-1723Real-time characterisation of nonlinear processes in the time domain is challenging. Here, Närhi et al. show that machine learning techniques can help overcome this limitation and use them to infer time-domain properties of optical fibre modulation instability from spectral intensity measurements.Mikko NärhiLauri SalmelaJuha ToivonenCyril BilletJohn M. DudleyGoëry GentyNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-11 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Mikko Närhi Lauri Salmela Juha Toivonen Cyril Billet John M. Dudley Goëry Genty Machine learning analysis of extreme events in optical fibre modulation instability
description	Real-time characterisation of nonlinear processes in the time domain is challenging. Here, Närhi et al. show that machine learning techniques can help overcome this limitation and use them to infer time-domain properties of optical fibre modulation instability from spectral intensity measurements.
format	article
author	Mikko Närhi Lauri Salmela Juha Toivonen Cyril Billet John M. Dudley Goëry Genty
author_facet	Mikko Närhi Lauri Salmela Juha Toivonen Cyril Billet John M. Dudley Goëry Genty
author_sort	Mikko Närhi
title	Machine learning analysis of extreme events in optical fibre modulation instability
title_short	Machine learning analysis of extreme events in optical fibre modulation instability
title_full	Machine learning analysis of extreme events in optical fibre modulation instability
title_fullStr	Machine learning analysis of extreme events in optical fibre modulation instability
title_full_unstemmed	Machine learning analysis of extreme events in optical fibre modulation instability
title_sort	machine learning analysis of extreme events in optical fibre modulation instability
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/2189e5907a1e496d9adc09f2e86e99a5
work_keys_str_mv	AT mikkonarhi machinelearninganalysisofextremeeventsinopticalfibremodulationinstability AT laurisalmela machinelearninganalysisofextremeeventsinopticalfibremodulationinstability AT juhatoivonen machinelearninganalysisofextremeeventsinopticalfibremodulationinstability AT cyrilbillet machinelearninganalysisofextremeeventsinopticalfibremodulationinstability AT johnmdudley machinelearninganalysisofextremeeventsinopticalfibremodulationinstability AT goerygenty machinelearninganalysisofextremeeventsinopticalfibremodulationinstability
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Machine learning analysis of extreme events in optical fibre modulation instability

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