{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T18:08:29Z","timestamp":1790014109052,"version":"4.0.1"},"reference-count":22,"publisher":"Optica Publishing Group","issue":"28","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFC3101402"],"award-info":[{"award-number":["2021YFC3101402"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2682025XJ020"],"award-info":[{"award-number":["2682025XJ020"]}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["Appl. Opt."],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:p>Efficient prediction of vector soliton propagation is necessary for parameter sweeps and system design in birefringent optical fibers. However, repeated simulations with the split-step Fourier method are computationally expensive. Neural operators have been widely used to accelerate simulations of nonlinear optical dynamics, but existing architectures often emphasize either global spectral modeling or local multiscale reconstruction. Here, we develop a surrogate model based on the U-Net-enhanced Fourier neural operator (U-FNO) architecture for efficiently predicting vector soliton propagation. The model embeds lightweight U-Net branches into selected Fourier layers. We compare it with a convolutional neural network, U-Net, DeepONet, and FNO on a simulation benchmark. U-FNO substantially improves upon the FNO backbone. The power-weighted absolute percentage error, power mean absolute percentage error, and terminal power root-mean-square error (RMSE) decrease by 28%, 32%, and 29%, respectively. Among the evaluated models, U-FNO achieves the lowest field mean absolute error and terminal power RMSE, and it is substantially more robust to input noise and to unseen temporal grid resolutions than the other high-accuracy models. Per-sample diagnostics show that larger gains tend to occur for more concentrated and sharper target fields. These results demonstrate the potential of U-FNO for efficient vector soliton propagation prediction in birefringent optical fibers.<\/jats:p>","DOI":"10.1364\/ao.606953","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T18:00:10Z","timestamp":1788199210000},"page":"9574","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient prediction of vector optical soliton propagation in birefringent fibers via an enhanced\u00a0Fourier neural operator"],"prefix":"10.1364","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2330-3835","authenticated-orcid":true,"given":"Xinyu","family":"Ji","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00hn7w693","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4795-6723","authenticated-orcid":true,"given":"Qi","family":"Li","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00hn7w693","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxi","family":"Li","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00hn7w693","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongtao","family":"Liu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00hn7w693","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Luo","sequence":"additional","affiliation":[{"name":"Southwest Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5046-5701","authenticated-orcid":true,"given":"Hongna","family":"Zhu","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00hn7w693","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"ao-65-28-9574-R1","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1063\/1.1654836","type":"journal-article","volume":"23","author":"Hasegawa","year":"1973","journal-title":"Appl. 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Li H. et al. , \u201c uFNO-vector-soliton ,\u201d GitHub ( 2026 ), https:\/\/github.com\/jixiaoge\/ufno-vector-soliton ."}],"container-title":["Applied Optics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/opg.optica.org\/viewmedia.cfm?URI=ao-65-28-9574&seq=0","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T17:15:45Z","timestamp":1790010945000},"score":1,"resource":{"primary":{"URL":"https:\/\/opg.optica.org\/abstract.cfm?URI=ao-65-28-9574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,21]]},"references-count":22,"journal-issue":{"issue":"28","published-online":{"date-parts":[[2026]]},"published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.1364\/ao.606953","relation":{},"ISSN":["1559-128X","2155-3165"],"issn-type":[{"value":"1559-128X","type":"print"},{"value":"2155-3165","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,21]]},"assertion":[{"value":"Optica Publishing Group","name":"publisher","label":"This article is maintained by"},{"value":"https:\/\/doi.org\/10.1364\/AO.606953","name":"articlelink","label":"Crossref DOI link to publisher maintained version"},{"value":"research-article","name":"content_type","label":"Article type"},{"value":"Screened by Similarity Check","name":"cross_check","label":"Similarity check"},{"value":"Yes","order":0,"name":"peer_reviewed","label":"Peer reviewed","group":{"name":"peer_review","label":"Peer review"}},{"value":"Single blind","order":1,"name":"review_process","label":"Review process","group":{"name":"peer_review","label":"Peer review"}},{"value":"28 May 2026","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"30 August 2026","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"21 September 2026","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}},{"value":"\u00a9 2026 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}