Machine Learning Assisted Worst-Case Radar Cross Section Optimisation Using Geometry-Aware Surrogate Modelling

Machine Learning Assisted Worst-Case Radar Cross Section Optimisation Using Geometry-Aware Surrogate Modelling | 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 Machine Learning Assisted Worst-Case Radar Cross Section Optimisation Using Geometry-Aware Surrogate Modelling Anand Rawat, Sanjeev Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8435404/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 Radar Cross Section (RCS) reduction is a key consideration in the early-stage design of low observable aerospace platforms. Direct optimisation of RCS using full-wave electromagnetic solvers is often computationally prohibitive when iterative design exploration is required. To address this challenge, this paper presents a software-oriented framework for worst-case RCS optimisation based on a physics-inspired synthetic benchmark model coupled with machine learning and genetic algorithm optimisation. A compact set of geometry and material descriptors, including platform dimensions, surface complexity, coating thickness, and dielectric loss tangent, is employed to parameterize the design space. A synthetic RCS benchmark model is used to generate training data capturing dominant qualitative scattering trends, which are subsequently learned by a Random Forest regression model. The trained surrogate enables rapid evaluation of candidate designs within a genetic algorithm framework that minimizes the worst-case RCS over the full angular domain. The proposed framework is implemented as a standalone graphical software tool supporting geometry input, surrogate model training, optimisation, visualization, and automated report generation. Results obtained within the synthetic benchmark en- vironment demonstrate stable optimisation behavior and physically consistent design trends, highlighting the utility of the approach for rapid design space exploration and methodological evaluation rather than final electromagnetic validation. Artificial Intelligence and Machine Learning Computational Physics Radar cross section worst-case optimisation surrogate modelling genetic algorithm synthetic benchmark Full Text Additional Declarations The authors declare no competing interests. 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. 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Modelling\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"DMSRDE","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":"Radar cross section, worst-case optimisation, surrogate modelling, genetic algorithm, 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Direct optimisation of RCS using full-wave electromagnetic solvers is often computationally prohibitive when iterative design exploration is required. To address this challenge, this paper presents a software-oriented framework for worst-case RCS optimisation based on a physics-inspired synthetic benchmark model coupled with machine learning and genetic algorithm optimisation.\u003c/p\u003e \u003cp\u003eA compact set of geometry and material descriptors, including platform dimensions, surface complexity, coating thickness, and dielectric loss tangent, is employed to parameterize the design space. A synthetic RCS benchmark model is used to generate training data capturing dominant qualitative scattering trends, which are subsequently learned by a Random Forest regression model. 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