FeaGPT: an End-to-End Agentic-AI for Finite Element Analysis

FeaGPT: an End-to-End Agentic-AI for Finite Element Analysis | 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 FeaGPT: an End-to-End Agentic-AI for Finite Element Analysis Yupeng Qi, Ran Xu, Xu Chu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8562963/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 Large language models (LLMs) are establishing new paradigms for engineering applications by enabling natural language control of complex computational workflows. This paper introduces FeaGPT, the first framework to achieve complete geometry-mesh-simulation workflows through conversational interfaces. Unlike existing tools that automate individual FEA components, FeaGPT implements a fully integrated Geometry-Mesh-Simulation-Analysis (GMSA) pipeline that transforms engineering specifications into validated computational results without manual intervention. The system interprets engineering intent, automatically generates physics-aware adaptive meshes, configures complete FEA simulations with proper boundary condition inference, and performs multiobjective analysis through closed-loop iteration. Experimental validation confirms complete end-to-end automation capability. Industrial turbocharger cases (7-blade compressor and 12-blade turbine at 110 000 rpm) demonstrate the system successfully transforms natural language specifications into validated CalculiX simulations, producing physically realistic results for rotating machinery analysis. Additional validation through 432 NACA airfoil configurations confirms scalability for parametric design exploration. These results demonstrate that natural language interfaces can effectively democratize access to advanced computational engineering tools while preserving analytical rigor. 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. 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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-8562963","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583613659,"identity":"30cdb911-3e99-4492-af8a-4f43fb7d0d52","order_by":0,"name":"Yupeng Qi","email":"","orcid":"","institution":"University of Stuttgart","correspondingAuthor":false,"prefix":"","firstName":"Yupeng","middleName":"","lastName":"Qi","suffix":""},{"id":583613660,"identity":"bbc7f74e-269a-4e65-902d-901866d27597","order_by":1,"name":"Ran Xu","email":"","orcid":"","institution":"University of Stuttgart","correspondingAuthor":false,"prefix":"","firstName":"Ran","middleName":"","lastName":"Xu","suffix":""},{"id":583613661,"identity":"869fcc3c-f704-486f-a456-2a259ae09cd1","order_by":2,"name":"Xu Chu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIie3RMQuCQBTA8ScH12K43iHkVzhxDT/LxYHNLSE0eBDcJLjWt0kcbglbAycR2qOprcAGp1O3hvsvxxt+3IMHYLP9aQhgPZzxJJL0zxxSzSDszp3XTt1WTBdll0IcAEm4kdATR/5ZNRHNKxReQYSSJBcj8QjHaKmaTUEEphIQB7KVRoJ7Umcq6BZvCdk4+f1y4R5B2JFQfcnIYjRvj75bi/Cci4hKpkPlPriRMC3Kl7uPA6bL9inTQ+AtEmYkAM5wczbpkDabzWYb6wP7sTgYlfZupgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Exeter","correspondingAuthor":true,"prefix":"","firstName":"Xu","middleName":"","lastName":"Chu","suffix":""}],"badges":[],"createdAt":"2026-01-09 16:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8562963/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8562963/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103839094,"identity":"1f5655d6-5884-4718-8a3b-5d8e0a5b4934","added_by":"auto","created_at":"2026-03-03 14:27:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3332467,"visible":true,"origin":"","legend":"","description":"","filename":"FEAGPTXUCHU.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8562963/v1_covered_5397d093-c9c7-4658-8ba6-e88719ae9cba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FeaGPT: an End-to-End Agentic-AI for Finite Element Analysis","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-8562963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8562963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLarge language models (LLMs) are establishing new paradigms for engineering applications by enabling natural language control of complex computational workflows. 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