We build AI and machine-learning methods and apply them to high-impact problems in biomedicine and engineering. Our work spans the full stack — from foundation and generative models, through scientific machine learning, to trustworthy, physics-aware AI — grounded in deep domain expertise in mechanics and simulation.
We develop machine-learning and in-silico models that bridge engineering and clinical medicine — turning medical images and patient data into actionable, decision-support tools. Our work spans cardiovascular disease, ophthalmology, and soft-tissue mechanics, combining learned models with physics-based simulation for reliable, patient-specific predictions.
Key topics:
Application highlights: Cardiovascular AI Retinal imaging In-silico medicine
We design neural architectures that learn the physics of engineering systems — graph neural operators, physics-informed neural networks, and surrogate models that replace expensive simulations while respecting the underlying mechanics. These methods make high-fidelity analysis fast enough for design, optimisation, and uncertainty quantification at scale.
Key topics:
Application highlights: Neural PDE solvers Wave-structure interaction Digital twins
We explore how modern AI — large foundation models, generative models, and agentic systems — can transform how engineering and scientific work gets done. From generative design and inverse problems to AI agents that drive simulation and analysis pipelines, we bring contemporary CS-style AI to bear on engineering practice.
Key topics:
Application highlights: Generative design AI agents Scientific tooling
Our AI methods are grounded in a strong foundation of computational mechanics. These earlier research directions remain part of the lab’s heritage — the publications and software stay fully available and continue to inform our scientific machine-learning work today.
Classical finite element methods Space & deployable structures Wave-structure interaction Soft-tissue & contact mechanics
See Former Areas for details.