SOURCE-LINKED INTELLIGENCE

Real-time Adhesion Trajectories & Inverse Design via Physics-Enhanced Machine Learning

CORDIS · observation · Publication date unknown

Real-time Adhesion Trajectories & Inverse Design via Physics-Enhanced Machine Learning Viscoelastic adhesive interfaces with tunable properties are central to emerging technologies in robotics, biomedicine, and smart materials. However, their design and control are severely constrained by the slow computational cost of classical contact mechanics and empirical models, which cannot deliver real-time predictions across complex, viscoelastic, and non-smooth surfaces. This project introduces a Physics-Enhanced Machine Learning (PEML) framework for the real-time design, prediction, and inverse optimization of tunable adhesive interfaces. The approach integrates high-fidelity simulations with advanced learning architectures, spanning Neural Controlled Differential Equations (NCDEs) and Neural Operators, while embedding physical principles such as viscoelastic constitutive laws, energy balances, and adhesion models. This hybrid strategy ensures physically consist

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recordType

award

status

SIGNED

region

EU

value

236128.88

unit

EUR