From ESN to Physical Reservoirs for Space Weather Forecasting

From ESN to Physical Reservoirs for Space Weather Forecasting | 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 From ESN to Physical Reservoirs for Space Weather Forecasting Ayush Gupta, Vipin Agarwal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7576213/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 Reliable prediction of space weather conditions, analogous in complexity and importance to terrestrial weather forecasting, is critical for protecting satellite infrastructure, communication systems, and power grids. Traditional numerical forecasting models, though powerful, struggle with high-dimensional chaotic dynamics, significant computational demands, and real-time applicability. Reservoir Computing (RC), a lightweight yet powerful framework utilizing recurrent neural dynamics, has recently emerged as an efficient alternative for forecasting complex, nonlinear phenomena. This study investigates and benchmarks five distinct reservoir computing architectures: Echo State Networks (ESNs), Nonlinear Vector Autoregressive (NVAR) reservoirs, the Lorenz system as a physical reservoir, and physically inspired mono- and bi-stable Duffing oscillator-based physical reservoir Computers (PRC). ESN and NVAR reservoir computers are popular and traditional reservoir computer algorithms, and Lorenz and Duffing oscillators are physically inspired reservoir computers. Each proposed model's predictive capability is rigorously evaluated using real-time solar wind magnetic field data, providing quantitative assessments and qualitative insights. Results reveal distinct advantages and limitations of each approach, highlighting that mono- and bi-stable Duffing oscillators can achieve performance close to traditional algorithm-based reservoirs in terms of accuracy and capturing multidimensional dependencies, while emphasizing the untapped potential of coupled oscillator-based PRC architectures. Reservoir computing Physical reservoir Lorenz System Duffing oscillator Space weather 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-7576213","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518893059,"identity":"c3fba30e-57f1-4107-87b3-2e59f6b24601","order_by":0,"name":"Ayush Gupta","email":"","orcid":"","institution":"University of Memphis","correspondingAuthor":false,"prefix":"","firstName":"Ayush","middleName":"","lastName":"Gupta","suffix":""},{"id":518893060,"identity":"28834c8b-7373-4071-b9c0-da51a7100903","order_by":1,"name":"Vipin Agarwal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACZgaGA0BKDsI2AFEJxGkxJkELFCQ2QLQTocXgOO/BwwUMdulr27vTHhcU1DHws+cY4NdymC/h8AyG5NxtZ85uN55hcJhBsucNfi2SzTwGh3kYmHO33cjdJs1jcIDB4AYBW6Ba6tPN7r8FaaljsCekhZ8ZrOVwgtkNXpAWYKBJENQC9AuPwXHDbWdytxuDtEuceVaAVwsb/9nDn3kqquXNjp/d9pjnT50cf3vyBrxaGBh4gBjiEjYYlxBAqGEjQvUoGAWjYBSMRAAAE7w/AnyKgqEAAAAASUVORK5CYII=","orcid":"","institution":"University of Memphis","correspondingAuthor":true,"prefix":"","firstName":"Vipin","middleName":"","lastName":"Agarwal","suffix":""}],"badges":[],"createdAt":"2025-09-09 17:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7576213/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7576213/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91943366,"identity":"1fbf4d01-cc2f-4eb6-8081-ed713ea5f587","added_by":"auto","created_at":"2025-09-23 04:45:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21685963,"visible":true,"origin":"","legend":"","description":"","filename":"PaperSpaceWeatherForecastingAGVA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7576213/v1/460a8247ab56614dcf1be5fc.pdf"},{"id":91943364,"identity":"d49aaf5e-d631-4966-b74f-6cca2d45b41a","added_by":"auto","created_at":"2025-09-23 04:45:48","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4398,"visible":true,"origin":"","legend":"","description":"","filename":"9b4d2d5324aa432d8d1c020f7372fb1f.json","url":"https://assets-eu.researchsquare.com/files/rs-7576213/v1/8b4b3c18fb2bdcb6ff783ecf.json"},{"id":94136059,"identity":"e0100df7-55d6-409c-9a7e-ae7bb1e75163","added_by":"auto","created_at":"2025-10-22 19:02:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23683360,"visible":true,"origin":"","legend":"","description":"","filename":"PaperSpaceWeatherForecastingAGVA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7576213/v1_covered_a7d06994-8b87-4814-bf24-825e5fca5e87.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From ESN to Physical Reservoirs for Space Weather Forecasting","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":"Reservoir computing, Physical reservoir, Lorenz System, Duffing oscillator, Space weather","lastPublishedDoi":"10.21203/rs.3.rs-7576213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7576213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Reliable prediction of space weather conditions, analogous in complexity and importance to terrestrial weather forecasting, is critical for protecting satellite infrastructure, communication systems, and power grids. Traditional numerical forecasting models, though powerful, struggle with high-dimensional chaotic dynamics, significant computational demands, and real-time applicability. Reservoir Computing (RC), a lightweight yet powerful framework utilizing recurrent neural dynamics, has recently emerged as an efficient alternative for forecasting complex, nonlinear phenomena. This study investigates and benchmarks five distinct reservoir computing architectures: Echo State Networks (ESNs), Nonlinear Vector Autoregressive (NVAR) reservoirs, the Lorenz system as a physical reservoir, and physically inspired mono- and bi-stable Duffing oscillator-based physical reservoir Computers (PRC). ESN and NVAR reservoir computers are popular and traditional reservoir computer algorithms, and Lorenz and Duffing oscillators are physically inspired reservoir computers. Each proposed model's predictive capability is rigorously evaluated using real-time solar wind magnetic field data, providing quantitative assessments and qualitative insights. Results reveal distinct advantages and limitations of each approach, highlighting that mono- and bi-stable Duffing oscillators can achieve performance close to traditional algorithm-based reservoirs in terms of accuracy and capturing multidimensional dependencies, while emphasizing the untapped potential of coupled oscillator-based PRC architectures.","manuscriptTitle":"From ESN to Physical Reservoirs for Space Weather Forecasting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 04:45:44","doi":"10.21203/rs.3.rs-7576213/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":"9f3d04c7-ddd5-4af3-8852-67d501336ac3","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T18:53:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 04:45:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7576213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7576213","identity":"rs-7576213","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}