AI-mechanical coupling and design

This research theme focuses on the development and application of hybrid modelling approaches combining mechanistic, physics-based models with data-driven, machine-learning techniques. The work spans complex physical and biomedical systems, with applications in clinical outcome (e.g., brain health, obstetrics, sport injury), generative design (e.g., implant, catheter), materials in extreme environments (e.g., shocked metals), or environmental building (e.g., zero-emission), among others. Overall, this axis of research aims to enable scalable and interpretable modelling frameworks across engineering and life-science applications.

Members involved

Alice Collier

Phoebe Haste

Elizabeth Hayman

Amelie Hylton

Marti Puig