Reference-less decomposition of highly multimode fibers using a physics-driven neural network

Reference-less decomposition of highly multimode fibers using a physics-driven neural network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Reference-less decomposition of highly multimode fibers using a physics-driven neural network Qian Zhang, Yuan Sui, Stefan Rothe, Nektarios Koukourakis, Juergen Czarske This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5110336/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Multimode fibers (MMFs) play an increasing role in the optical communication field, as well as in ultra-thin imaging systems and fiber lasers. The characterization of light propagation properties through active and passive MMFs attracts high interest as many linear and nonlinear optical phenomena fundamentally depend on the interplay of many different spatial modes. Access to the exact modal amplitude and phase weights, namely mode decomposition (MD), helps to describe the present state and brings a better understanding of physical effects, driving further technological advancements on MMF-based telecommunication, endoscopy, or amplifiers. Here we show that using an untrained neural network, which assumes a simple physical model as a prior, we achieve MD with more than 98 % accuracy with up to 5,796 spatial modes. For the first time, we demonstrate that a deep learning-based MD method can work on a 1-km-long MMF for communication. We present our network framework and results using synthetic data and experimental data measured on passive MMFs as well as on an MMF amplifier. This method holds great promise for applications in fiber lasers, endoscopic metrology, and reference-free calibration techniques in fiber-based classical communication and single-photon quantum key distribution. Physical sciences/Optics and photonics/Applied optics/Fibre optics and optical communications Physical sciences/Optics and photonics/Optical techniques/Imaging and sensing Physical sciences/Physics/Techniques and instrumentation/Characterization and analytical techniques multimode fiber mode decomposition deep learning physics-informed phase retrieval fiber laser Full Text Additional Declarations There is NO Competing Interest. Supplementary Files PhyMoNetsupplement20240917.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5110336","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":364757349,"identity":"36155ad9-ec32-48d3-91e1-3307a4caac2d","order_by":0,"name":"Qian 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The characterization of light propagation properties through active and passive MMFs attracts high interest as many linear and nonlinear optical phenomena fundamentally depend on the interplay of many different spatial modes. Access to the exact modal amplitude and phase weights, namely mode decomposition (MD), helps to describe the present state and brings a better understanding of physical effects, driving further technological advancements on MMF-based telecommunication, endoscopy, or amplifiers.\r\nHere we show that using an untrained neural network, which assumes a simple physical model as a prior, we achieve MD with more than 98 % accuracy with up to 5,796 spatial modes. For the first time, we demonstrate that a deep learning-based MD method can work on a 1-km-long MMF for communication. We present our network framework and results using synthetic data and experimental data measured on passive MMFs as well as on an MMF amplifier. 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