Research and application of ROM based on Res-PINNs neural network in fluid system

Research and application of ROM based on Res-PINNs neural network in fluid system | 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 Research and application of ROM based on Res-PINNs neural network in fluid system Yuhao Liu, Junjie Hou, Ping Wei, Jie Jin, Renjie Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4211045/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract The reduced-order-model(ROM) method provides a strong support for the rapid iteration and simulation verification of supporting fluid system design. This study focuses on the problems of gradient disappearance or explosion and incomplete learning of flow field characteristics in the training process of PINN ROM. Based on PINN model, an innovative ROM Res-PINNs is proposed. By embedding ResNet module into PINN neural network structure, it strives to improve the training stability of the model while retaining physical knowledge. In addition, parallel network structure is added to the model to improve its perception and learning ability of flow field state.At last, in order to verify the validity of the proposed model, two classical fluid problems, the flow around a cylinder and Vortex-Induced Vibration(VIV), are selected to compare and verify the proposed Res-PINNs model. The results show that Res-PINNs can reconstruct the flow field state more accurately, effectively overcome the problems of gradient disappearance or explosion and poor learning ability of PINN model during training, and provide a new solution for the application of deep learning order reduction method in aerospace system modeling and simulation. Physical sciences/Engineering/Aerospace engineering Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Computer science fluid system PINN ResNet parallel network ROM Full Text Additional Declarations No competing interests reported. Supplementary Files DATA.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Aug, 2024 Reviews received at journal 19 Aug, 2024 Reviewers agreed at journal 18 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviews received at journal 12 Aug, 2024 Reviewers agreed at journal 12 Jul, 2024 Reviewers agreed at journal 12 Jul, 2024 Reviewers invited by journal 10 Jul, 2024 Editor assigned by journal 03 Jul, 2024 Editor invited by journal 14 May, 2024 Submission checks completed at journal 14 May, 2024 First submitted to journal 03 Apr, 2024 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-4211045","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":305092571,"identity":"897c58bd-3f3e-4688-8150-a70eaa19a130","order_by":0,"name":"Yuhao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACNv7mww8SeGx47JsZGx8k/LAhrIVP4liawQeZNBkD9uZmg4c9aYS1yDHkGEjOsDlsY8BzvE3yAdthIhzGcMbAmCfnMI+5RGJbRQLPYQb+9u4E/FqY2woe85xJ57Gckdh2I8EinUHizNkNBGw5vMGYt8eah+EGSAuPNYOBRC4hLQkG0rz/mMFaChLYmInRkgL0Po8zj8GZg20MCWzORGgBBzJPGo9ke2OzRGJPGg9Bv8j3Q6LSnp+Z/eHHHz9s5Pjbe/FrwQA8pCkfBaNgFIyCUYAVAADux0gBpLQCFQAAAABJRU5ErkJggg==","orcid":"","institution":"China Aerospace Academy of Systems Science and Engineering","correspondingAuthor":true,"prefix":"","firstName":"Yuhao","middleName":"","lastName":"Liu","suffix":""},{"id":305092572,"identity":"b5a235dc-f28e-4c7a-a212-d9ca6d67ebed","order_by":1,"name":"Junjie Hou","email":"","orcid":"","institution":"China Aerospace Academy of Systems Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Hou","suffix":""},{"id":305092573,"identity":"c3a75ce6-8480-409f-8702-1b69b4d4dc39","order_by":2,"name":"Ping Wei","email":"","orcid":"","institution":"China Academy of Space Technology","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Wei","suffix":""},{"id":305092574,"identity":"c5532557-e0ff-44c3-970d-ce8f3ef0b783","order_by":3,"name":"Jie Jin","email":"","orcid":"","institution":"China Aerospace Academy of Systems Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Jin","suffix":""},{"id":305092575,"identity":"c27eba72-bbe6-4798-ac75-eed0b73cb5b4","order_by":4,"name":"Renjie Zhang","email":"","orcid":"","institution":"China Aerospace Academy of Systems Science and Engineering","correspondingAuthor":false,"prefix":"","firstName":"Renjie","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-04-03 08:14:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4211045/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4211045/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56984951,"identity":"fdc20fc2-9929-48b7-9c60-5babdac44a76","added_by":"auto","created_at":"2024-05-23 04:49:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1210576,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchandapplicationofROMbasedonResPINNsneuralnetworkinfluidsystem.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4211045/v1_covered_78b7714e-bee9-4757-85be-833ecdd18d72.pdf"},{"id":56984585,"identity":"56a3a07f-e860-4e13-92e7-409d49925e03","added_by":"auto","created_at":"2024-05-23 04:41:17","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8499749,"visible":true,"origin":"","legend":"","description":"","filename":"DATA.zip","url":"https://assets-eu.researchsquare.com/files/rs-4211045/v1/f2414ec09b796f3ed7533ef1.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research and application of ROM based on Res-PINNs neural network in fluid system","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"fluid system, PINN, ResNet, parallel network, ROM","lastPublishedDoi":"10.21203/rs.3.rs-4211045/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4211045/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe reduced-order-model(ROM) method provides a strong support for the rapid iteration and simulation verification of supporting fluid system design. This study focuses on the problems of gradient disappearance or explosion and incomplete learning of flow field characteristics in the training process of PINN ROM. Based on PINN model, an innovative ROM Res-PINNs is proposed. By embedding ResNet module into PINN neural network structure, it strives to improve the training stability of the model while retaining physical knowledge. In addition, parallel network structure is added to the model to improve its perception and learning ability of flow field state.At last, in order to verify the validity of the proposed model, two classical fluid problems, the flow around a cylinder and Vortex-Induced Vibration(VIV), are selected to compare and verify the proposed Res-PINNs model. The results show that Res-PINNs can reconstruct the flow field state more accurately, effectively overcome the problems of gradient disappearance or explosion and poor learning ability of PINN model during training, and provide a new solution for the application of deep learning order reduction method in aerospace system modeling and simulation.\u003c/p\u003e","manuscriptTitle":"Research and application of ROM based on Res-PINNs neural network in fluid system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 04:41:13","doi":"10.21203/rs.3.rs-4211045/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2024-08-30T05:14:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-19T17:15:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148230167624867853931548605595321507031","date":"2024-08-18T14:41:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279344508070482051711537570874038763391","date":"2024-08-16T15:36:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-12T09:22:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215484404192581895881979171486951222639","date":"2024-07-12T12:58:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292717945026394453025868799399058904966","date":"2024-07-12T09:09:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-10T06:56:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-03T05:29:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-14T12:29:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-14T12:24:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-03T08:13:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e16e63a6-ad8c-4b97-bde2-d67851296a61","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":32212459,"name":"Physical sciences/Engineering/Aerospace engineering"},{"id":32212460,"name":"Physical sciences/Engineering/Mechanical engineering"},{"id":32212461,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2024-05-23T04:41:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 04:41:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4211045","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4211045","identity":"rs-4211045","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}