A Physics-Informed Operator Learning Benchmark for Thermal-Mechanical Surrogate Modeling in WA-DED

A Physics-Informed Operator Learning Benchmark for Thermal-Mechanical Surrogate Modeling in WA-DED | 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 A Physics-Informed Operator Learning Benchmark for Thermal-Mechanical Surrogate Modeling in WA-DED Donghong Ding, Mengjiao Li, haochen mu, mingxuan tian, Yuhan Ding This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8624648/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 Operator-learning surrogates are emerging as a practical route to real-time simulation for wire-arc directed energy deposition (WA-DED). However, predictive accuracy is often degraded when jointly learning transient reversible quantities (e.g., elastic and thermal strain) and history-dependent responses (e.g., plastic strain and stress). To address the resulting feature interference, we establish a benchmark for evaluating Physics-Informed Operator Learning (PIOL) pathways via constitutive behavior, including FEM labeling, decoupled modeling, and PIOL evaluating. The PIOL framework uses a shared trunk to represent geometry-conditioned basis functions, while employing heterogeneous branches matched to constitutive behavior: a lightweight CNN-MLP branch for transient elastic response and a CNN-LSTM branch for history-dependent plasticity and stress. A constitutive constraint term further regularizes the thermal-strain prediction. Finally, through thermally calibrated FEM simulated dataset across multiple process conditions, the PIOL scheme evaluated on the benchmark demonstrate improved stress prediction accuracy relative to baseline variants, with millisecond-scale inference suitable for online digital-twin deployment. Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Computational science WA-DED Machine Learning Operator Learning Thermal-Mechanical Modeling Real-time Prediction Full Text Additional Declarations There is NO Competing Interest. 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. 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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-8624648","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":576920764,"identity":"b8c29a94-daae-4687-bb61-8ae00fc1b2e9","order_by":0,"name":"Donghong 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WA-DED","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"WA-DED, Machine Learning, Operator Learning, Thermal-Mechanical Modeling, Real-time Prediction","lastPublishedDoi":"10.21203/rs.3.rs-8624648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8624648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOperator-learning surrogates are emerging as a practical route to real-time simulation for wire-arc directed energy deposition (WA-DED). However, predictive accuracy is often degraded when jointly learning transient reversible quantities (e.g., elastic and thermal strain) and history-dependent responses (e.g., plastic strain and stress). To address the resulting feature interference, we establish a benchmark for evaluating Physics-Informed Operator Learning (PIOL) pathways via constitutive behavior, including FEM labeling, decoupled modeling, and PIOL evaluating. The PIOL framework uses a shared trunk to represent geometry-conditioned basis functions, while employing heterogeneous branches matched to constitutive behavior: a lightweight CNN-MLP branch for transient elastic response and a CNN-LSTM branch for history-dependent plasticity and stress. A constitutive constraint term further regularizes the thermal-strain prediction. Finally, through thermally calibrated FEM simulated dataset across multiple process conditions, the PIOL scheme evaluated on the benchmark demonstrate improved stress prediction accuracy relative to baseline variants, with millisecond-scale inference suitable for online digital-twin deployment.\u003c/p\u003e","manuscriptTitle":"A Physics-Informed Operator Learning Benchmark for Thermal-Mechanical Surrogate Modeling in WA-DED","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 15:17:23","doi":"10.21203/rs.3.rs-8624648/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"922e2019-cee0-408c-9855-b9c17e55eefc","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61379150,"name":"Physical sciences/Engineering/Mechanical engineering"},{"id":61379151,"name":"Physical sciences/Mathematics and computing/Computational science"}],"tags":[],"updatedAt":"2026-03-30T20:51:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 15:17:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8624648","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8624648","identity":"rs-8624648","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}