Metadata
Title
AI-mechanical coupling and design
Category
undergraduate
UUID
aca6ec87d2ab49d58c813da7f4f7775c
Source URL
https://jerugroup.eng.ox.ac.uk/research/ai-mechanical-coupling-and-design
Parent URL
https://jerugroup.eng.ox.ac.uk/
Crawl Time
2026-03-09T03:22:22+00:00
Rendered Raw Markdown
# AI-mechanical coupling and design

**Source**: https://jerugroup.eng.ox.ac.uk/research/ai-mechanical-coupling-and-design
**Parent**: https://jerugroup.eng.ox.ac.uk/

## 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