Application of Physics-Informed Neural Networks for Simulating Mass-Spring-Damper Systems 

Application of Physics-Informed Neural Networks for Simulating Mass-Spring-Damper Systems | 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 Application of Physics-Informed Neural Networks for Simulating Mass-Spring-Damper Systems Arup Kumar Sahoo, Sandeep Kumar, S. Chakraverty This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4015566/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 Dynamical problems are generally governed by a set of linear/non-linear differential equations (DEs). A large amount of prior physical information in the form of DEs plays an important role in simulation of dynamical systems. However, the traditional data-hungry machine learning models fail to express insightful scientific information from the data. Most of the implementations of neural networks are to perform non-linear mapping from input space to target space. However, Physics-informed neural networks (PINNs) can bridge the gap between scientific computing and data-hungry models. This paper exploits a new application of PINNs for approximating the behaviour of mass-spring-damper systems, showcasing how PINNs can effectively blend scientific principles with data-driven modeling. In this regard, we present solutions of two realistic application problems using PINNs. The accuracy of the predicted displacements of objects is established through results from literatures. ANN PINN Physics-informed machine learning Scientific machine learning Mass-spring-damper system Tanh 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-4015566","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276443444,"identity":"5a877125-3e7e-4ba3-8edf-f282745db29d","order_by":0,"name":"Arup Kumar Sahoo","email":"","orcid":"","institution":"National Institute of Technology Rourkela","correspondingAuthor":false,"prefix":"","firstName":"Arup","middleName":"Kumar","lastName":"Sahoo","suffix":""},{"id":276443445,"identity":"97633e60-8575-443c-ab58-c00d9534c0d5","order_by":1,"name":"Sandeep Kumar","email":"","orcid":"","institution":"National Institute of Technology Rourkela","correspondingAuthor":false,"prefix":"","firstName":"Sandeep","middleName":"","lastName":"Kumar","suffix":""},{"id":276443446,"identity":"32572a72-53c4-403c-99c8-9a8ff34a2728","order_by":2,"name":"S. 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