Addressing Discontinuity in Finite Element - Control Volume Based Liquid Injection Moulding Simulations using Neural Network Surrogates

Addressing Discontinuity in Finite Element - Control Volume Based Liquid Injection Moulding Simulations using Neural Network Surrogates | 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 Research Article Addressing Discontinuity in Finite Element - Control Volume Based Liquid Injection Moulding Simulations using Neural Network Surrogates Nicholas Wright, Oliver Maclaren, Piaras Kelly, Suresh Advani, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6816143/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2025 Read the published version in International Journal of Material Forming → Version 1 posted You are reading this latest preprint version Abstract Optimisation and machine learning techniques are increasingly used with net-shape composites forming methods like resin transfer moulding (RTM) for process monitoring, control, and defect detection. However, the standard finite element-control volume modelling approach used in these simulations is discontinuous with respect to model parameters and time. We demonstrate that the use of a neural network surrogate, designed with smooth activation layers, can approximate these models, while eliminating any nonsmooth or discontinuous behaviour. To illustrate the benefits of this method, we use Bayesian inference to predict permeability and race tracking strengths from pressure measurements, where the neural network prevents discontinuities from propagating to the posterior, forming smooth posterior distributions that incorporate the model approximation error. Resin transfer moulding permeability estimation moving boundary problems neural networks Bayesian inference Bayesian approximation error Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2025 Read the published version in International Journal of Material Forming → 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-6816143","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":467570166,"identity":"037ad7a6-2f0b-42a3-8ceb-fbbf33e79750","order_by":0,"name":"Nicholas Wright","email":"data:image/png;base64,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","orcid":"","institution":"University of Auckland","correspondingAuthor":true,"prefix":"","firstName":"Nicholas","middleName":"","lastName":"Wright","suffix":""},{"id":467570168,"identity":"b9e1e1bc-53f9-4e29-b2af-e8bd8976ddf5","order_by":1,"name":"Oliver Maclaren","email":"","orcid":"","institution":"University of Auckland","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Maclaren","suffix":""},{"id":467570170,"identity":"1b339af9-6892-42fa-9269-55ab600a2271","order_by":2,"name":"Piaras Kelly","email":"","orcid":"","institution":"University of Auckland","correspondingAuthor":false,"prefix":"","firstName":"Piaras","middleName":"","lastName":"Kelly","suffix":""},{"id":467570175,"identity":"b4369ea8-6f8a-4a8c-9acf-4e1e71d154ac","order_by":3,"name":"Suresh Advani","email":"","orcid":"","institution":"University of Delaware","correspondingAuthor":false,"prefix":"","firstName":"Suresh","middleName":"","lastName":"Advani","suffix":""},{"id":467570178,"identity":"4a729210-5ee8-4b5a-b94c-5282fdf4e52a","order_by":4,"name":"Ruanui Nicholson","email":"","orcid":"","institution":"University of Auckland","correspondingAuthor":false,"prefix":"","firstName":"Ruanui","middleName":"","lastName":"Nicholson","suffix":""}],"badges":[],"createdAt":"2025-06-04 04:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6816143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6816143/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12289-025-01954-z","type":"published","date":"2025-10-29T15:58:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95041413,"identity":"044a25eb-eead-4e24-a160-87542db14052","added_by":"auto","created_at":"2025-11-03 16:11:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":604782,"visible":true,"origin":"","legend":"","description":"","filename":"WrightDiscontinuities.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6816143/v1_covered_a6ca1cc9-5420-48de-8fe2-e5b12646d3cd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Addressing Discontinuity in Finite Element - Control Volume Based Liquid Injection Moulding Simulations using Neural Network Surrogates","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Resin transfer moulding, permeability estimation, moving boundary problems, neural networks, Bayesian inference, Bayesian approximation error","lastPublishedDoi":"10.21203/rs.3.rs-6816143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6816143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOptimisation and machine learning techniques are increasingly used with net-shape composites forming methods like resin transfer moulding (RTM) for process monitoring, control, and defect detection. 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