Artificial neural networks for nonlinear pulse shaping in optical fibers
•Machine-learning predicts the nonlinear propagation of temporal and spectral profiles.•Propagation in nonlinear fibers with both normal and anomalous dispersion is studied.•The neural network can retrieve the parameters of the nonlinear propagation.•Various initial pulse shapes as well as initially...
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| Veröffentlicht in: | Optics and laser technology 2020-11, Vol.131, p.106439, Article 106439 |
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| container_title | Optics and laser technology |
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| creator | Boscolo, Sonia Finot, Christophe |
| description | •Machine-learning predicts the nonlinear propagation of temporal and spectral profiles.•Propagation in nonlinear fibers with both normal and anomalous dispersion is studied.•The neural network can retrieve the parameters of the nonlinear propagation.•Various initial pulse shapes as well as initially chirped pulses are investigated.
We use a supervised machine-learning model based on a neural network to predict the temporal and spectral intensity profiles of the pulses that form upon nonlinear propagation in optical fibers with both normal and anomalous second-order dispersion. We also show that the model is able to retrieve the parameters of the nonlinear propagation from the pulses observed at the output of the fiber. Various initial pulse shapes as well as initially chirped pulses are investigated. |
| doi_str_mv | 10.1016/j.optlastec.2020.106439 |
| format | Article |
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We use a supervised machine-learning model based on a neural network to predict the temporal and spectral intensity profiles of the pulses that form upon nonlinear propagation in optical fibers with both normal and anomalous second-order dispersion. We also show that the model is able to retrieve the parameters of the nonlinear propagation from the pulses observed at the output of the fiber. Various initial pulse shapes as well as initially chirped pulses are investigated.</description><identifier>ISSN: 0030-3992</identifier><identifier>EISSN: 1879-2545</identifier><identifier>DOI: 10.1016/j.optlastec.2020.106439</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Artificial neural networks ; Machine learning ; Neural networks ; Nonlinear propagation ; Optical fibers ; Optics ; Physics ; Pulse propagation ; Pulse shaping</subject><ispartof>Optics and laser technology, 2020-11, Vol.131, p.106439, Article 106439</ispartof><rights>2020 Elsevier Ltd</rights><rights>Copyright Elsevier BV Nov 2020</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c426t-1b99e801074934934a6127504d84205f68b5c59206c68957200b91b03706d8443</citedby><cites>FETCH-LOGICAL-c426t-1b99e801074934934a6127504d84205f68b5c59206c68957200b91b03706d8443</cites><orcidid>0000-0002-0755-5995</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0030399220310720$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://hal.science/hal-02484123$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Boscolo, Sonia</creatorcontrib><creatorcontrib>Finot, Christophe</creatorcontrib><title>Artificial neural networks for nonlinear pulse shaping in optical fibers</title><title>Optics and laser technology</title><description>•Machine-learning predicts the nonlinear propagation of temporal and spectral profiles.•Propagation in nonlinear fibers with both normal and anomalous dispersion is studied.•The neural network can retrieve the parameters of the nonlinear propagation.•Various initial pulse shapes as well as initially chirped pulses are investigated.
We use a supervised machine-learning model based on a neural network to predict the temporal and spectral intensity profiles of the pulses that form upon nonlinear propagation in optical fibers with both normal and anomalous second-order dispersion. We also show that the model is able to retrieve the parameters of the nonlinear propagation from the pulses observed at the output of the fiber. Various initial pulse shapes as well as initially chirped pulses are investigated.</description><subject>Artificial neural networks</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Nonlinear propagation</subject><subject>Optical fibers</subject><subject>Optics</subject><subject>Physics</subject><subject>Pulse propagation</subject><subject>Pulse shaping</subject><issn>0030-3992</issn><issn>1879-2545</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNqFkEFLxDAQhYMouK7-BguePHSdpEnaHMuirrDgRc8hzaZuam1q0q74702t7FUYeDB882bmIXSNYYUB87tm5fqhVWEwekWATF1OM3GCFrjIRUoYZadoAZBBmglBztFFCA0AUM6yBdqUfrC11Va1SWdG_yvDl_PvIamdTzrXtbYzyif92AaThL3qbfeW2C6Ja62OfG0r48MlOqtVJK7-dIleH-5f1pt0-_z4tC63qaaEDymuhDAFYMipyKZSHJOcAd0VlACreVExzQQBrnkhWE4AKoEryHLgEaHZEt3OvnvVyt7bD-W_pVNWbsqtnHpAaEExyQ44sjcz23v3OZowyMaNvovnSUJpERfklEUqnyntXQje1EdbDHKKWDbyGLGcIpZzxHGynCdNfPhgjZdBW9Nps7Pe6EHunP3X4wcZjoYM</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Boscolo, Sonia</creator><creator>Finot, Christophe</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><general>Elsevier</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>L7M</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-0755-5995</orcidid></search><sort><creationdate>20201101</creationdate><title>Artificial neural networks for nonlinear pulse shaping in optical fibers</title><author>Boscolo, Sonia ; Finot, Christophe</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c426t-1b99e801074934934a6127504d84205f68b5c59206c68957200b91b03706d8443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial neural networks</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Nonlinear propagation</topic><topic>Optical fibers</topic><topic>Optics</topic><topic>Physics</topic><topic>Pulse propagation</topic><topic>Pulse shaping</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Boscolo, Sonia</creatorcontrib><creatorcontrib>Finot, Christophe</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Optics and laser technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Boscolo, Sonia</au><au>Finot, Christophe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial neural networks for nonlinear pulse shaping in optical fibers</atitle><jtitle>Optics and laser technology</jtitle><date>2020-11-01</date><risdate>2020</risdate><volume>131</volume><spage>106439</spage><pages>106439-</pages><artnum>106439</artnum><issn>0030-3992</issn><eissn>1879-2545</eissn><abstract>•Machine-learning predicts the nonlinear propagation of temporal and spectral profiles.•Propagation in nonlinear fibers with both normal and anomalous dispersion is studied.•The neural network can retrieve the parameters of the nonlinear propagation.•Various initial pulse shapes as well as initially chirped pulses are investigated.
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| ispartof | Optics and laser technology, 2020-11, Vol.131, p.106439, Article 106439 |
| issn | 0030-3992 1879-2545 |
| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Artificial neural networks Machine learning Neural networks Nonlinear propagation Optical fibers Optics Physics Pulse propagation Pulse shaping |
| title | Artificial neural networks for nonlinear pulse shaping in optical fibers |
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