Decision-making under Uncertainty for COmplex systems (DUCO) team

To pioneer uncertainty-aware digital twins that empower decision-makers to navigate risk and complexity across interconnected systems — at any scale.

We pursue this vision through:

• Developing scalable risk methods for complex, interconnected systems.
• Unifying isolated scientific models within a shared computational framework, enabling them to complement, challenge, and reconcile each other.

To this end, our research foucses on addressing scalability and validation challenges. We study Bayesian network, systems engineering, uncertainty quantification (UQ), reliability and risk assessment, and risk-based optimisation.
We aim to develop software tools applicable across diverse systems and risks. Our interests include, but are not limited to, transport networks, structural systems, energy grids, and process plants, subjected to risks such as earthquakes, floods, wildfires, localised incidents, and deterioration.

Research

Applications