Semi-parameterized Nonlinear Optical Operator Predict Ultrafast Spectral across Multiple Power Domains

Semi-parameterized Nonlinear Optical Operator Predict Ultrafast Spectral across Multiple Power Domains | 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 Semi-parameterized Nonlinear Optical Operator Predict Ultrafast Spectral across Multiple Power Domains Wenjun Liu, Zhiyang Zhang, Xiwei Huang, Xiaowei Xing, Muwei Liu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8242662/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 Ultrafast laser spectroscopy is a central tool in ultrafast optics. However the evolution of intracavity pulses is governed by strongly coupled multiphysics processes on sub-nanosecond time scales, making their behavior difficult to predict and control. Existing deep learning approaches are typically limited to modeling a single pump power or simulated spectra, and fail to capture transient spectral dynamics under varying operating conditions. Here we propose a semi-parameterized approach that embeds the dispersion and nonlinear structure of the generalized nonlinear Schrödinger equation into an intelligent computation framework, and we design a semi-parameterized nonlinear optical operator (SNOO) as a concrete realization of this idea. This study achieves for the first time multi-power ultrafast spectral prediction in lasers, reconstructing the spectral dynamics of Q-switched and microresonator soliton mode-locked pulses at 128 round trips intervals on sub-nanosecond timescales. Using only the initial 128 round trips, SNOO accurately predicts the spectral evolution over the subsequent 12800 round trips, precisely capturing the periodicity and amplitude of microcomb lines. Furthermore SNOO enables super-resolution spectral prediction across an eight-fold oscilloscope sampling-rate gap for four distinct classes of modelocked pulses, recovering high-speed sideband features unresolved by low-sampling-rate oscilloscopes. In all results, SNOO achieves state-of-the-art performance in relative error, similarity, and signal-to-noise ratio across nine models (five data-driven models and four neural operator models), while using fewer trainable parameters (as few as 10%). Our semi-parameterized structure provides a novel design paradigm for intelligent models in ultrafast optics, paving the way toward surpassing current instrumentation limits and enabling future terahertz-band spectral measurements. Physical sciences/Optics and photonics/Optical physics/Nonlinear optics Physical sciences/Optics and photonics/Lasers, LEDs and light sources/Fibre lasers Physical sciences/Optics and photonics/Optical physics/Solitons Ultrafast optics Spectrum prediction Neural operator Physical machine learning Superresolution Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterial.pdf Supplementary Material for manuscript 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8242662","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":578178225,"identity":"a188cc17-30ff-4095-b838-6b632e89cd6e","order_by":0,"name":"Wenjun 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However the evolution of intracavity pulses is governed by strongly coupled multiphysics processes on sub-nanosecond time scales, making their behavior difficult to predict and control. Existing deep learning approaches are typically limited to modeling a single pump power or simulated spectra, and fail to capture transient spectral dynamics under varying operating conditions. Here we propose a semi-parameterized approach that embeds the dispersion and nonlinear structure of the generalized nonlinear Schrödinger equation into an intelligent computation framework, and we design a semi-parameterized nonlinear optical operator (SNOO) as a concrete realization of this idea. This study achieves for the first time multi-power ultrafast spectral prediction in lasers, reconstructing the spectral dynamics of Q-switched and microresonator soliton mode-locked pulses at 128 round trips intervals on sub-nanosecond timescales. 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