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

Project description

Real-time design and prediction of viscoelastic adhesive interfaces

Viscoelastic adhesive interfaces with tunable properties are crucial to a variety of key technologies being developed in the biomedicine, smart materials and robotics sectors, among others. However, our ability to design and control them has been limited by the high computational cost of numerical models and classical contact mechanics, which are unable to deliver real-time predictions for surfaces that are non-smooth, complex, and viscoelastic. Supported by the Marie Skłodowska-Curie Actions programme, the REAL-ADHERE project will develop a physics-enhanced machine learning (PEML) framework that enables real-time design, prediction, and inverse optimisation of tunable adhesive interfaces. The project will adopt a hybrid strategy to ensure interpretable, physically consistent results while accelerating design and improving efficiency.

Objective

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 consistent and interpretable results while leveraging acceleration of ML inference for design. Furthermore, the project advances beyond forward modeling by employing generative design for the inverse problem, allowing the tailored engineering of adhesive surfaces under physical and manufacturing constraints. Prototype fabrication and validation will bridge the gap between simulation and experiment, ensuring demonstrable feasibility. The expected outcomes include: (i) the first real-time, physics-consistent tool for adhesive interface dynamics, (ii) design strategies generalizable to non-smooth and multi-asperity surfaces, and (iii) transformative applications in adaptive gripping, biomedical devices, and switchable adhesion systems. The project thus addresses urgent scientific and technological challenges by combining fundamental physics, cutting-edge ML, and experimental validation to pave the way for sustainable and high-impact adhesion technologies.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

TECHNISCHE UNIVERSITAT BERLIN
Net EU contribution
€ 236 128,88
Address
STRASSE DES 17 JUNI 135
10623 Berlin
Germany
Region
Berlin Berlin Berlin
Activity type
Higher or Secondary Education Establishments
Links
Total cost
No data

Partners (2)