AI-Based Physics Geometry Adaptation Framework for Accurate and Generalizable Hemodynamic Modeling

AI-Based Physics Geometry Adaptation Framework for Accurate and Generalizable Hemodynamic Modeling | 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 AI-Based Physics Geometry Adaptation Framework for Accurate and Generalizable Hemodynamic Modeling Heye Zhang, Weiyuan Lin, Zhifan Gao, Xiujian Liu, Guang Yang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6779296/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Three-dimensional Hemodynamic modeling and application demonstrate significant value. In the paper, we propose a 3D hemodynamic computation framework (PGAF) integrating variational inference and optimal transport theory. Variational inference enables adaptive inference of reasonable initial solutions for unseen cases by linking vascular features and boundary conditions with latent hemodynamic patterns. Optimal transport theory constructs a metric between predicted flow fields and established hemodynamic solution space, which is established through analysis of large-scale computational fluid dynamics (CFD) simulation data, to constrain global physical deviation. PGAF implements a dynamic coupling correction mechanism where the hemodynamic results are iteratively refined through the optimization，so it ensures global physical consistency in vessels and calculation accuracy during evolution. We collect 2500 different vessels from 7 public image datasets and perform 8000 CFD simulations under various boundary conditions. PGAF is ultimately validated and evaluated in 500 cases containing invasive catheter measurements. The results demonstrate the ability of our method to accurately predict the velocity and pressure distributions within the vessels, demonstrating high generalizability, robustness, and precision under different boundary conditions. Health sciences/Cardiology/Cardiovascular biology/Cardiovascular diseases Physical sciences/Mathematics and computing/Computational science Hemodynamic Modeling Artificial Intelligence Physics-Guided Learning Vascular Applications Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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. 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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-6779296","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":472303117,"identity":"386ea6e5-370b-4e48-aa07-d2559990cbc8","order_by":0,"name":"Heye 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