Shihong Xu, Xinyi Xu, Run Zhou, Jiahao Zhang, Qun Zhang, Lu Zhang
{"title":"基于物理信息的偏振分复用光纤传输系统确定性建模神经网络。","authors":"Shihong Xu, Xinyi Xu, Run Zhou, Jiahao Zhang, Qun Zhang, Lu Zhang","doi":"10.1364/AO.571796","DOIUrl":null,"url":null,"abstract":"<p><p>The coupled nonlinear Schrödinger equation (CNLSE) governs signal propagation in polarization division multiplexed (PDM) optical fiber systems, yet poses significant numerical challenges. This paper introduces physics-informed neural networks (PINNs) as a novel framework for deterministic modeling of PDM transmission. Through validation across single-pulse evolution, communication sequences, and full PDM systems, PINNs demonstrate deterministic accuracy (<i>R</i><i>M</i><i>S</i><i>E</i>=0.0044∼0.0129 and <i>s</i><i>p</i><i>e</i><i>c</i><i>t</i><i>r</i><i>a</i><i>l</i><i>e</i><i>r</i><i>r</i><i>o</i><i>r</i><i>s</i><4<i>%</i>) while overcoming traditional limitation. They eliminate the split-step Fourier method (SSFM)'s step-size dependencies and data-driven methods' statistical uncertainties. By preserving physical determinism through embedded PDE constraints, PINNs establish a new paradigm, to our knowledge, for reliable fiber-optic system modeling.</p>","PeriodicalId":101299,"journal":{"name":"Applied optics","volume":"64 26","pages":"7827-7833"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Physics-informed neural networks for deterministic modeling of polarization division multiplexed fiber transmission systems.\",\"authors\":\"Shihong Xu, Xinyi Xu, Run Zhou, Jiahao Zhang, Qun Zhang, Lu Zhang\",\"doi\":\"10.1364/AO.571796\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The coupled nonlinear Schrödinger equation (CNLSE) governs signal propagation in polarization division multiplexed (PDM) optical fiber systems, yet poses significant numerical challenges. This paper introduces physics-informed neural networks (PINNs) as a novel framework for deterministic modeling of PDM transmission. Through validation across single-pulse evolution, communication sequences, and full PDM systems, PINNs demonstrate deterministic accuracy (<i>R</i><i>M</i><i>S</i><i>E</i>=0.0044∼0.0129 and <i>s</i><i>p</i><i>e</i><i>c</i><i>t</i><i>r</i><i>a</i><i>l</i><i>e</i><i>r</i><i>r</i><i>o</i><i>r</i><i>s</i><4<i>%</i>) while overcoming traditional limitation. They eliminate the split-step Fourier method (SSFM)'s step-size dependencies and data-driven methods' statistical uncertainties. By preserving physical determinism through embedded PDE constraints, PINNs establish a new paradigm, to our knowledge, for reliable fiber-optic system modeling.</p>\",\"PeriodicalId\":101299,\"journal\":{\"name\":\"Applied optics\",\"volume\":\"64 26\",\"pages\":\"7827-7833\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied optics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/AO.571796\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/AO.571796","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Physics-informed neural networks for deterministic modeling of polarization division multiplexed fiber transmission systems.
The coupled nonlinear Schrödinger equation (CNLSE) governs signal propagation in polarization division multiplexed (PDM) optical fiber systems, yet poses significant numerical challenges. This paper introduces physics-informed neural networks (PINNs) as a novel framework for deterministic modeling of PDM transmission. Through validation across single-pulse evolution, communication sequences, and full PDM systems, PINNs demonstrate deterministic accuracy (RMSE=0.0044∼0.0129 and spectralerrors<4%) while overcoming traditional limitation. They eliminate the split-step Fourier method (SSFM)'s step-size dependencies and data-driven methods' statistical uncertainties. By preserving physical determinism through embedded PDE constraints, PINNs establish a new paradigm, to our knowledge, for reliable fiber-optic system modeling.