Data-Driven and Physics-Guided Neural Approaches for LCR Circuit Modeling: A Comparative Analysis

Data-Driven and Physics-Guided Neural Approaches for LCR Circuit Modeling: A Comparative 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 Article Data-Driven and Physics-Guided Neural Approaches for LCR Circuit Modeling: A Comparative Analysis Tanneeru Gopi Sai Ram, Nimai Sarkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9455563/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract In this paper, a comparative study is carried out between Recurrent Neural Network Long Short Term Memory (RNN LSTM) models and Physics Informed Neural Networks (PINNs) for modeling the dynamic response of an LCR circuit. The circuit dynamics are first formulated in the form of an integro differential equation to capture the memory characteristics of the system. An approximate analytical solution for the charge response is obtained using the Sumudu Transform combined with the Adomian Decomposition Method. The obtained charge profile is then used as training data for both RNN LSTM and PINN models to predict the exact temporal evolution of the circuit charge. The performance of the models is evaluated using prediction accuracy, convergence behaviour, and error analysis. Simulation results show that the RNN LSTM model provides better prediction accuracy and more stable learning compared to the PINN model for the considered LCR system. The study demonstrates that data driven sequential deep learning approaches can effectively model complex electrical circuit dynamics and may offer practical advantages in real time prediction and intelligent circuit analysis. Physical sciences/Engineering Physical sciences/Mathematics and computing Fractional LCR Circuit Integro–Differential Equation Sumudu Transform Adomian Decomposition Method Recurrent Neural Network Long Short Term Memory Physics-Informed Neural Networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 22 May, 2026 Reviews received at journal 20 May, 2026 Reviews received at journal 15 May, 2026 Reviews received at journal 12 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers invited by journal 07 May, 2026 Editor assigned by journal 07 May, 2026 Editor invited by journal 07 May, 2026 Submission checks completed at journal 28 Apr, 2026 First submitted to journal 28 Apr, 2026 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-9455563","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":641526977,"identity":"0d59620a-2ea3-4f6e-9403-b2dd9533eeb9","order_by":0,"name":"Tanneeru Gopi Sai Ram","email":"","orcid":"","institution":"VIT-AP University","correspondingAuthor":false,"prefix":"","firstName":"Tanneeru","middleName":"Gopi Sai","lastName":"Ram","suffix":""},{"id":641526981,"identity":"aaf7bb62-582b-4967-98ca-2e87a565ad5e","order_by":1,"name":"Nimai Sarkar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYDACZiBOYGCQAzPgIsRoMWZgZkYSIQYkNhCt1JydO/HDA4a69A3H+Q9+LmCwk2dg5z2AV4tlM+9miQSGw7kbDjMzS89gSDZsYOZLwKvF4DDvBqCWA7kzm5kZpHkYmBMYmHkMCGnZ/CMB6DDJZmbm3zwM9URp2Qa0hTmBn5mZDWjLYeK0WCQYHDbsZ2Y2s55hcNywjaCW82c33/xRUSfPxn/w8e2Cimp5fv4z+LVANUIoZhCDjQj1CEBsZI6CUTAKRsEIAwDL8jNBcLir9gAAAABJRU5ErkJggg==","orcid":"","institution":"VIT-AP University","correspondingAuthor":true,"prefix":"","firstName":"Nimai","middleName":"","lastName":"Sarkar","suffix":""}],"badges":[],"createdAt":"2026-04-18 08:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9455563/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9455563/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109435241,"identity":"4b63aa71-427d-4de1-b5a8-05f03b83debc","added_by":"auto","created_at":"2026-05-18 05:59:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":341774,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9455563/v1_covered_1cb2bfb7-cfaf-4d3f-8b3a-fa37dac4e376.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data-Driven and Physics-Guided Neural Approaches for LCR Circuit Modeling: A Comparative Analysis","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":"Fractional LCR Circuit, Integro–Differential Equation, Sumudu Transform, Adomian Decomposition Method, Recurrent Neural Network, Long Short Term Memory,Physics-Informed Neural Networks","lastPublishedDoi":"10.21203/rs.3.rs-9455563/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9455563/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In this paper, a comparative study is carried out between Recurrent Neural Network Long Short Term Memory (RNN LSTM) models and Physics Informed Neural Networks (PINNs) for modeling the dynamic response of an LCR circuit. The circuit dynamics are first formulated in the form of an integro differential equation to capture the memory characteristics of the system. An approximate analytical solution for the charge response is obtained using the Sumudu Transform combined with the Adomian Decomposition Method. The obtained charge profile is then used as training data for both RNN LSTM and PINN models to predict the exact temporal evolution of the circuit charge. The performance of the models is evaluated using prediction accuracy, convergence behaviour, and error analysis. Simulation results show that the RNN LSTM model provides better prediction accuracy and more stable learning compared to the PINN model for the considered LCR system. The study demonstrates that data driven sequential deep learning approaches can effectively model complex electrical circuit dynamics and may offer practical advantages in real time prediction and intelligent circuit analysis.","manuscriptTitle":"Data-Driven and Physics-Guided Neural Approaches for LCR Circuit Modeling: A Comparative Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 05:59:50","doi":"10.21203/rs.3.rs-9455563/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-22T15:41:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-21T02:06:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T15:44:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T02:09:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183329912102508035314970066970904820596","date":"2026-05-13T01:29:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29335211335342041847490612626261508428","date":"2026-05-12T00:40:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241116746923856355277813719764112979352","date":"2026-05-07T13:15:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-07T12:45:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T12:35:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-07T12:25:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-28T05:49:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-28T05:47:44+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":"018489c8-a0c3-4712-8b32-ffbc3076e806","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-22T15:41:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-21T02:06:56+00:00","index":66,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T15:44:29+00:00","index":65,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T02:09:14+00:00","index":63,"fulltext":""},{"type":"reviewerAgreed","content":"183329912102508035314970066970904820596","date":"2026-05-13T01:29:38+00:00","index":62,"fulltext":""},{"type":"reviewerAgreed","content":"29335211335342041847490612626261508428","date":"2026-05-12T00:40:33+00:00","index":60,"fulltext":""},{"type":"reviewerAgreed","content":"241116746923856355277813719764112979352","date":"2026-05-07T13:15:09+00:00","index":54,"fulltext":""},{"type":"reviewersInvited","content":"39","date":"2026-05-07T12:45:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-07T12:35:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-07T12:25:06+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":68238824,"name":"Physical sciences/Engineering"},{"id":68238825,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-22T15:54:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 05:59:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9455563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9455563","identity":"rs-9455563","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}