Multitask 3D Convolutional Neural Network–Based Multiphysics Design Exploration of Triply Periodic Minimal Surface Bone Scaffolds

Multitask 3D Convolutional Neural Network–Based Multiphysics Design Exploration of Triply Periodic Minimal Surface Bone Scaffolds | 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 Multitask 3D Convolutional Neural Network–Based Multiphysics Design Exploration of Triply Periodic Minimal Surface Bone Scaffolds Innocent Bwengye, Emmanuel Ahishakiye, William Wasswa, Johnes Obungoloch This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8670608/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Triply periodic minimal surface (TPMS) scaffolds have emerged as promising candidates for bone tissue engineering due to their ability to balance mechanical stiffness, mass transport, and flow-induced shear environments. However, accurate evaluation of these coupled properties typically relies on computationally intensive finite element and computational fluid dynamics simulations, which limits large-scale exploration of the TPMS design space. In this study, we propose a multitask three-dimensional convolutional neural network (3D-CNN) surrogate model for the concurrent prediction of key effective properties of TPMS bone scaffolds, including apparent elastic modulus, intrinsic permeability, effective diffusivity, and a wall shear stress (WSS)–based exposure metric, directly from voxelized scaffold geometries. A dataset of 1,000 unique TPMS designs spanning gyroid, Schwarz-P, and diamond families was generated and labeled using multiphysics simulations, with additional instances obtained through symmetry-preserving transformations. The surrogate model employs a physically informed seven-channel geometric representation and joint learning across tasks to capture shared structure–property relationships. On a held-out test set, the multitask surrogate demonstrated robust predictive performance across all targets, achieving coefficients of determination up to 0.53-0.55 for transport and mechanical properties in challenging intermediate-porosity regimes, while reducing root mean squared error by approximately 40–55% compared to classical analytical models. Relative to single-task CNNs, multitask learning further reduced prediction errors by 7-12%, particularly for transport- and shear-related quantities. Family-wise and representative-design analyses revealed physically consistent topology-dependent trends, with Schwarz-P structures favoring stiffness, gyroid architectures promoting transport performance, and diamond scaffolds offering balanced trade-offs. Pareto-based exploration confirmed that no single topology is universally optimal, underscoring the need for application-specific scaffold selection. TPMS scaffolds multitask 3D-CNN surrogate modeling permeability effective diffusivity bone tissue engineering Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers invited by journal 26 Jan, 2026 Editor invited by journal 24 Jan, 2026 Editor assigned by journal 23 Jan, 2026 Submission checks completed at journal 23 Jan, 2026 First submitted to journal 22 Jan, 2026 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-8670608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582066713,"identity":"827f7ab0-1fe9-460e-a618-e137fe525ab4","order_by":0,"name":"Innocent 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