Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media

Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media | 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 Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media M. Giselle Fernández-Godino, Meir H. Shachar, Kevin Korner, Jonathan L. Belof, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7820496/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 The ability to predict how shock waves traverse porous and architected materials is a key challenge in planetary defense and in the pursuit of inertial fusion energy. Yet capturing pore collapse, anomalous Hugoniot responses, and localized heating—phenomena that strongly influence asteroid deflection or fusion ignition—has remained a major challenge despite recent advances in single-field and reduced representations. We introduce a multi-field spatio-temporal model (MSTM) that unifies seven coupled fields—pressure, density, temperature, energy, material distribution, and two velocity components—into a single autoregressive surrogate. Trained on high-fidelity hydrocode data, MSTM captures nonlinear shock-driven dynamics across porous and architected configurations, achieving mean errors of 1.4% and 3.2% respectively, all while delivering over three orders of magnitude in speedup. MSTM reduces mean-squared error and structural dissimilarity by 94% relative torelative to single-field spatio-temporal models. This advance transforms problems once considered intractable into tractable design studies, establishing a practical framework for optimizing meso-structured materials in planetary impact mitigation and inertial fusion energy. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics 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-7820496","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":547751657,"identity":"ae0a5a0d-8f80-439b-81d8-1edc6be49486","order_by":0,"name":"M. 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