Development of a Novel Machine Learning-Based Adaptive Resampling Algorithm for Nuclear Data Processing

Development of a Novel Machine Learning-Based Adaptive Resampling Algorithm for Nuclear Data Processing | 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 Development of a Novel Machine Learning-Based Adaptive Resampling Algorithm for Nuclear Data Processing Alexander Hashemi, Rafael Macián-Juan, Martin Ohlerich This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6693833/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Efficient processing of nuclear cross-sections data is critical for advanced reactor physics and safety assessments. Existing workflows of using nuclear data in Hierarchical Data Format, version 5 (HDF5 format) rely on intermediate file formats, such as A Compact ENDF (ACE) files generated via NJOY, which introduce inefficiencies in nuclear data processing. This work presents two novel computational techniques that streamline nuclear data processing and modification. First, a machine learning-based resampling algorithm is presented for nuclear cross-section data stored in HDF5 format, designed to intelligently retain critical threshold points while reducing data redundancy. Second, a direct HDF5 modification framework is introduced, eliminating the need for legacy file conversion steps and enabling direct edits to OpenMC-compatible nuclear data libraries. This methodology employs an adaptive resampling strategy that dynamically adjusts point densities across diverse neutron energy regions, preserving resonance structures and threshold behaviors while achieving significant data compression. Benchmarking against established models—such as K-Nearest Neighbors and Gaussian Processes—indicates that the ML-based approach offers lower errors and enhanced computational efficiency. This integrated framework improves nuclear data accessibility and expedites simulations, reactor core design, uncertainty quantification, and neutronics analysis. Physical sciences/Energy science and technology/Nuclear energy Physical sciences/Physics Physical sciences/Physics/Nuclear physics Nuclear Data Continuous Energy Cross-Section Machine Learning HDF5 Python OpenMC Adaptive Resampling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Jun, 2025 Reviews received at journal 08 Jun, 2025 Reviews received at journal 29 May, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers agreed at journal 28 May, 2025 Reviewers invited by journal 28 May, 2025 Editor invited by journal 27 May, 2025 Editor assigned by journal 23 May, 2025 Submission checks completed at journal 22 May, 2025 First submitted to journal 18 May, 2025 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. 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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-6693833","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463801293,"identity":"e012bb5a-52dd-4f18-8c61-6f94b108e7cb","order_by":0,"name":"Alexander Hashemi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIie3RMUvEMBTA8VcK7ZLcranV9is0BM4h4hdxuSNgl3Z3u8LBc9H9Nr/GjVcenMt9AMVFEdxEHTPIWQoniOQON4f8IBkCf15CADzvP4r7PewWAwgagChuticO4a+E0V8TEGZ3MpyFK2EXOjtuePthFydng+RtdXAHOnMlgiIjr9elOlwOTMrX5zWmJkorKJVzDDH5yJEmc2BFGiBtE5o0jiKn4Xv7iTTtEmUtbmpMqE+mrqQgFjx3U8YC2EhwXNYowj4Zu+4lKVLqCEs5D9lIczQ1MqN0VZTSNSW7nb0kr6hzEV+pe4un9c1l+/RQXejc+fxvPz+i2B94nud5bl9dYkxk4wTt9wAAAABJRU5ErkJggg==","orcid":"","institution":"Technical University of Munich","correspondingAuthor":true,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Hashemi","suffix":""},{"id":463801294,"identity":"0cb25a9c-b2ff-402d-aed9-7ac4584338b1","order_by":1,"name":"Rafael Macián-Juan","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Macián-Juan","suffix":""},{"id":463801295,"identity":"43f86842-06ca-4f29-9776-62bf6f7af982","order_by":2,"name":"Martin Ohlerich","email":"","orcid":"","institution":"Bavarian Academy of Sciences and Humanities","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Ohlerich","suffix":""}],"badges":[],"createdAt":"2025-05-18 22:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6693833/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6693833/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-18674-8","type":"published","date":"2025-09-17T15:57:24+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91889871,"identity":"a8bb9a4f-1c02-49b7-b6f6-27e20fb0c51a","added_by":"auto","created_at":"2025-09-22 16:03:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4994733,"visible":true,"origin":"","legend":"","description":"","filename":"DevelopmentofaNovelMachinelearningAlgorithmforNuclearDataProcessing.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6693833/v1_covered_8ee61960-497a-4bb9-af16-471db4f6b9fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a Novel Machine Learning-Based Adaptive Resampling Algorithm for Nuclear Data Processing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Nuclear Data, Continuous Energy Cross-Section, Machine Learning, HDF5, Python, OpenMC, Adaptive Resampling","lastPublishedDoi":"10.21203/rs.3.rs-6693833/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6693833/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEfficient processing of nuclear cross-sections data is critical for advanced reactor physics and safety assessments. Existing workflows of using nuclear data in Hierarchical Data Format, version 5 (HDF5 format) rely on intermediate file formats, such as A Compact ENDF (ACE) files generated via NJOY, which introduce inefficiencies in nuclear data processing. This work presents two novel computational techniques that streamline nuclear data processing and modification. First, a machine learning-based resampling algorithm is presented for nuclear cross-section data stored in HDF5 format, designed to intelligently retain critical threshold points while reducing data redundancy. Second, a direct HDF5 modification framework is introduced, eliminating the need for legacy file conversion steps and enabling direct edits to OpenMC-compatible nuclear data libraries. This methodology employs an adaptive resampling strategy that dynamically adjusts point densities across diverse neutron energy regions, preserving resonance structures and threshold behaviors while achieving significant data compression. Benchmarking against established models—such as K-Nearest Neighbors and Gaussian Processes—indicates that the ML-based approach offers lower errors and enhanced computational efficiency. 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