000161709 001__ 161709
000161709 005__ 20251017144628.0
000161709 0247_ $$2doi$$a10.1016/j.mtcomm.2025.113018
000161709 0248_ $$2sideral$$a144332
000161709 037__ $$aART-2025-144332
000161709 041__ $$aeng
000161709 100__ $$aDeng, Bincan
000161709 245__ $$aTransfer learning-enabled viscosity prediction for HAMA/GelMA hybrid hydrogels
000161709 260__ $$c2025
000161709 5060_ $$aAccess copy available to the general public$$fUnrestricted
000161709 5203_ $$aArtificial intelligence is transforming the development and design of complex biomedical materials as for example functionalized hydrogels. However, the high experimental costs associated with developing these materials require innovative strategies to reduce data demands for predictive modeling. This study introduces a novel transfer learning approach, termed as Partial Layer Freezing and Re-initialization (PLFRi), designed specifically for small-sample scenarios to predict the viscosity of hybrid hydrogels. Using a multilayer perceptron architecture, we incorporate higher-order nonlinear and weakly nonlinear modules to enable domain adaptation from a precursor system (Hyaluronic Acid/Gelatin) to a target system (Hyaluronic Acid Methacryloyl/Gelatin Methacryloyl). The PLFRi strategy achieves a 119 % improvement in prediction accuracy under limited training data conditions compared to direct modeling. Further optimization of the target-to-source data ratio reveals a trade-off region (7–10 %) between predictive accuracy and cost-efficiency. Additionally, directional sampling of characteristic shear rates enhances model performance (4.56 % improvement in coefficient of determination) and underscores the potential for expanding spatial dimensions into predictive modeling. This study establishes a novel transfer learning paradigm for intelligent hydrogel design, providing a universal and resource-efficient framework for advancing biomaterial development.
000161709 540__ $$9info:eu-repo/semantics/embargoedAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000161709 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000161709 700__ $$aLópez Lasaosa, Fernando
000161709 700__ $$aChen, Dingding
000161709 700__ $$aHe, Yiyan
000161709 700__ $$aXuan, Chen
000161709 700__ $$aCui, Yuwen
000161709 700__ $$0(orcid)0000-0001-8741-6452$$aDoblaré, Manuel$$uUniversidad de Zaragoza
000161709 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000161709 773__ $$g47 (2025), 113018 [9 p.]$$tMaterials Today Communications$$x2352-4928
000161709 8564_ $$s20097822$$uhttps://zaguan.unizar.es/record/161709/files/texto_completo.pdf$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2027-06-07
000161709 8564_ $$s1528615$$uhttps://zaguan.unizar.es/record/161709/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint$$zinfo:eu-repo/date/embargoEnd/2027-06-07
000161709 909CO $$ooai:zaguan.unizar.es:161709$$particulos$$pdriver
000161709 951__ $$a2025-10-17-14:24:59
000161709 980__ $$aARTICLE