Graph neural network for colliding particles with an application to sea ice floe modeling

Graph neural network for colliding particles with an application to sea ice floe modeling | 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 Graph neural network for colliding particles with an application to sea ice floe modeling Ruibiao Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6243496/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 This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions, including collisions. This concept is developed within a one-dimensional framework as a foundational step. Traditional numerical methods, while effective, are computationally intensive and less scalable. By utilizing GNNs, the proposed model, termed the Collision-captured Network (CN), integrates data assimilation (DA) techniques to effectively learn and predict sea ice dynamics under various conditions. The approach was validated using synthetic data, both with and without observed data points, and it was found that the model accelerates the rendering of trajectories without compromising accuracy. This advancement offers a more efficient tool for forecasting in marginal ice zones (MIZ) and highlights the potential of combining machine learning with data assimilation for more effective and efficient modeling. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Climate sciences/Ocean sciences/Physical oceanography Full Text Additional Declarations No competing interests reported. Supplementary Files Graphneuralnetworkforcollidingparticleswithanapplicationtoseaicefloemodeling.zip 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6243496","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":441010139,"identity":"a1abe45a-e6f9-4a66-8b3d-5ad80e260c7a","order_by":0,"name":"Ruibiao Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFACxjaGBBAtwcD4ACKSQLwWZgMitTCwQSgJBjYJorSYz0hue/CgxoZBfnaPWcXPHYcZ+NlzDBh+tuHWInMjsd0g4VgaA+OcM2Y3e88cZpDseWPA2ItHi4REYptEAtthBmaJHLMbvG2HGQxuAG3hJajl32GgR3LMCv8CtdgDtTD+JaQlEaiSB6iFGWyLRI4BM15beB62GyT2pfFISKQVS8u2pfNInHlWcFjmHB4t7OnPHv74ZiMnPyN548e3bdZy/O3JGx++KcOtBQZ4UBgHCGsYBaNgFIyCUYAPAAAPq0lDQsnKEwAAAABJRU5ErkJggg==","orcid":"","institution":"Australian National University","correspondingAuthor":true,"prefix":"","firstName":"Ruibiao","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-03-17 10:08:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6243496/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6243496/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80787912,"identity":"ef3e00bf-c427-464b-807b-93751d24dd60","added_by":"auto","created_at":"2025-04-17 06:10:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6371179,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptforGraphneuralnetworkforcollidingparticleswithanapplicationtoseaicefloemodeling.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6243496/v1_covered_8ee0dedb-d305-4e2a-afdf-48b707a7ab50.pdf"},{"id":80759913,"identity":"cef3d769-aa4a-4aa5-8d91-ace42bda0333","added_by":"auto","created_at":"2025-04-16 19:06:13","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1605668737,"visible":true,"origin":"","legend":"","description":"","filename":"Graphneuralnetworkforcollidingparticleswithanapplicationtoseaicefloemodeling.zip","url":"https://assets-eu.researchsquare.com/files/rs-6243496/v1/8c7e4cc08abeb6a8962361ce.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Graph neural network for colliding particles with an application to sea ice floe modeling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6243496/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6243496/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions, including collisions. 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