Understanding the fundamental dynamics of complex systems through mathematics, modelling and experiment, understanding the boundaries of their stability, then discovering new methods of control
Engineering has long sought to understand and predict the response of systems to stimuli and the limits of their stability. The challenge is intensifying as systems are becoming ever larger and more interconnected, as they are pushed to achieve higher performances, and as control is decentralised. The ability to create complex systems is outrunning the ability to guarantee and optimise their behaviour. Engineers in almost all domains are facing this challenge whether designing engines, electricity grids, bridges, communication networks, supply chains or autonomous systems.
The mission aims to take a world-lead in finding principled methods for modelling multi-physics systems that can enable robust predictions for optimal design. The methods will make efficient use of models of varying fidelity based on first principles and those that are data-driven. In the field of robotics and autonomous systems, the ambition is to achieve this control and optimisation through machine learning in real time.
- Applied Mechanics group
- Control group
- Computational and Biological Learning group
- Other connections to the bioengineering theme:
- Electronics, Power and Energy Conversion Group
- Distributed Information and Automation Laboratory