Neural Optimization Machine for Analytical Function Optimization: Application to Auxetic Metamaterial Design

Neural Optimization Machine for Analytical Function Optimization: Application to Auxetic Metamaterial Design | 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 Neural Optimization Machine for Analytical Function Optimization: Application to Auxetic Metamaterial Design Vishal Singh, Rajnish Mallick, Dineshkumar Harursampath, Sharanjeet Dhawan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7924648/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 In this study, a novel neural network-based optimization framework, called as Neural Optimization Machine (NOM) is presented toaddress constrained, unconstrained, and multi-objective optimization problems to design optimal auxetic metamaterial structures.Unlike conventional gradient-based and other traditional evolutionary solvers, the NOM constructs a surrogate model of theobjective function and integrates it into a neural architecture. NOM embeds the trained neural surrogate as the objective function,achieving end-to-end differentiable optimization. This yields high accuracy with reduced computation time (up to 20-25% fasterthan Genetic Algorithm). NOM’s performance is tested comprehensively on test functions (such as McCormick, Rosenbrock,and Mishra’s Bird). The framework is validated with computational experiments through optimization of a re-entrant auxeticmetamaterial structure design, achieving an optimal negative Poisson’s Ratio, ν = -1.55, showcasing NOM’s direct applicability toengineering structures, viz. a monolithic smart morphing wing. The results highlight NOM’s versatility and scalability, paving theway for broader applications in topology optimization, aerodynamic design, and material science. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing 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-7924648","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":548196589,"identity":"ce78ef99-f634-4d65-be0b-c9dc2e459b56","order_by":0,"name":"Vishal Singh","email":"","orcid":"","institution":"Indian Institute of Science Bangalore","correspondingAuthor":false,"prefix":"","firstName":"Vishal","middleName":"","lastName":"Singh","suffix":""},{"id":548196590,"identity":"4c852f54-29e7-4415-a4df-584ba96d0bc1","order_by":1,"name":"Rajnish Mallick","email":"","orcid":"","institution":"Indian Institute of Information Technology, Design and 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