Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods

Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods | 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 Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods Afrah Frea, Saiful Khan, Reza Daryani, Emre Cenk Ersan, Mustafa Serdar Celebi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6734948/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 We introduce neural network architectures that combine physics-informed neural networks (PINNs) with the immersed boundary method (IBM) to solve fluid-structure interaction (FSI) problems. Our approach features two distinct architectures: a Single-FSI network with a unified parameter space, and an innovative Eulerian-Lagrangian network that maintains separate parameter spaces for fluid and structure domains. We study each architecture using standard Tanh and adaptive B-spline activation functions. Empirical studies on a 2D cavity flow problem involving a moving solid structure show that the Eulerian-Lagrangian architecture performs significantly better. The adaptive B-spline activation further enhances accuracy by providing locality-aware representation near boundaries. While our methodology shows promising results in predicting the velocity field, pressure recovery remains challenging due to the absence of explicit force-coupling constraints in the current formulation. Our findings underscore the importance of domain-specific architectural design and adaptive activation functions for modeling FSI problems within the PINN framework. Fluid Structure Interaction (FSI) Immersed Boundary Method (IBM) Physics Informed Neural Networks (PINN) Activation Full Text Additional Declarations No competing interests reported. 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-6734948","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470884397,"identity":"1586cc80-d709-4263-acbb-bdb078d01112","order_by":0,"name":"Afrah Frea","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBAC9gYQaWAjxwbmshGhhecAiCxIM2Zjg2rhIU7Lh0OJDcRrYT/78MEPgwPpffI9Bgwfyg4z2EsfIKCFJ93YsMfgTm4bG48B44xzhxl4+BLwa7FnSGOT4DF4BtbCzNsG1ELIZTz8z9h//jE4nM4G0vKXKC0SaWzMPAaHE8BaGInT8oxZWsYgzbCNLa3gYM+5dB6eMwQdlsb48c0fG3n55sMbH/wos5Zj7yGgBQUcYCAiWkbBKBgFo2AUEAEApAU2R/i0XQ4AAAAASUVORK5CYII=","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":true,"prefix":"","firstName":"Afrah","middleName":"","lastName":"Frea","suffix":""},{"id":470884398,"identity":"f02eb486-22a4-417a-a0b2-0724d3d7e3ab","order_by":1,"name":"Saiful Khan","email":"","orcid":"","institution":"Science and Technology Facilities Council","correspondingAuthor":false,"prefix":"","firstName":"Saiful","middleName":"","lastName":"Khan","suffix":""},{"id":470884399,"identity":"1e2593b9-3f2e-4d55-9d8e-b52cb71003c5","order_by":2,"name":"Reza Daryani","email":"","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Daryani","suffix":""},{"id":470884400,"identity":"6af0577a-e0da-4b4a-bc1e-cad0c1121584","order_by":3,"name":"Emre Cenk Ersan","email":"","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":false,"prefix":"","firstName":"Emre","middleName":"Cenk","lastName":"Ersan","suffix":""},{"id":470884401,"identity":"9ad83346-1538-49e2-87ff-c2e6f7acff9b","order_by":4,"name":"Mustafa Serdar Celebi","email":"","orcid":"","institution":"Istanbul Technical University","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"Serdar","lastName":"Celebi","suffix":""}],"badges":[],"createdAt":"2025-05-23 17:53:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6734948/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6734948/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89031133,"identity":"fab83086-2567-49b8-b757-cc80f8f2000c","added_by":"auto","created_at":"2025-08-14 02:24:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5851287,"visible":true,"origin":"","legend":"","description":"","filename":"vjwdvtrjpxcbfzhbzptrjsvvrgsprgnd.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6734948/v1_covered_19ac847a-e55a-4db3-b6bc-3d4a3242e1ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods","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":"Fluid Structure Interaction (FSI), Immersed Boundary Method (IBM), Physics Informed Neural Networks (PINN), Activation","lastPublishedDoi":"10.21203/rs.3.rs-6734948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6734948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We introduce neural network architectures that combine physics-informed neural networks (PINNs) with the immersed boundary method (IBM) to solve fluid-structure interaction (FSI) problems. 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