Shihong Xu, Xinyi Xu, Run Zhou, Jiahao Zhang, Qun Zhang, Lu Zhang
{"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}
引用次数: 0
Abstract
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.