Maghami, AliAliMaghamiStender, MertenMertenStenderPapangelo, AntonioAntonioPapangelo2025-08-182025-08-182025-08-05International Journal of Solids and Structures 322: 113584 (2025)https://hdl.handle.net/11420/57020Predicting the adhesive properties of viscoelastic Hertzian contacts is crucial for diverse engineering applications, including robotics, biomechanics, and advanced material design. This study introduces a novel physics-augmented machine learning (PA-ML) framework as a hybrid approach to study the maximum adherence force of a Hertzian indenter unloaded from a viscoelastic substrate, bridging the gap between analytical models and data-driven solutions. The PA-ML model is capable of rapidly predicting the pull-off force in an Hertzian profile unloaded from a broad band viscoelastic material, with varying Tabor parameter, preload and retraction rate. Compared to previous models, the PA-ML approach provides fast yet accurate predictions in a wide range of conditions, properly predicting the effective surface energy and the work-to-pull-off. The integration of the analytical model provides critical guidance to the PA-ML framework, supporting physically consistent predictions. We demonstrate that physics augmentation enhances predictive accuracy, reducing mean squared error (MSE) while increasing model interpretability. We provide data-driven and PA-ML models for real-time predictions of the adherence force in soft materials like silicons and elastomers opening to the possibility to integrate PA-ML into materials and interface design. The models are openly available on Zenodo and GitHub.en0020-76832025Elsevierhttps://creativecommons.org/licenses/by/4.0/Broad-band viscoelasticityMachine learningPhysic augmented MLPredictive modelingViscoelastic adhesiveTechnology::620: Engineering::620.1: Engineering Mechanics and Materials ScienceTechnology::660: Chemistry; Chemical EngineeringJournal Articlehttps://doi.org/10.15480/882.1578210.1016/j.ijsolstr.2025.11358410.15480/882.15782Journal Article