Research project: Hybrid modelling and nonlinear dynamics in aircraft design

This is a potential research project for new research students. It is intended to be a starting point for discussion/further investigation rather than a complete research plan. If you are interested in working with me, see opportunities for new researchers. Keywords: aeroelastics, scientific machine learning, control-based continuation, nonlinear dynamics There is a drive towards high-efficiency and high-performance aircraft. This requires lighter and more fuel-efficient structural designs, which are often more flexible than traditional rigid designs....

2023-11-03

Research project: Online learning for tactile robotics

This is a potential research project for new research students. It is intended to be a starting point for discussion/further investigation rather than a complete research plan. If you are interested in working with me, see opportunities for new researchers. Keywords: online learning, machine learning, tactile robotics, generative models Machine learning (ML) equips robots with the ability to learn from and adapt to new environments, enhancing autonomy and efficiency in complex tasks....

2023-11-03

Research project: Surrogate modelling and machine learning for electrical power systems

This is a potential research project for new research students. It is intended to be a starting point for discussion/further investigation rather than a complete research plan. If you are interested in working with me, see opportunities for new researchers. Keywords: surrogate modelling, machine learning, reservoir computing, power electronics Designing new devices, particularly in electrical power conversion for renewable energy, is often challenging because of constraints on mass, volume, and cost....

2023-11-03

Broad areas for potential projects

SciML and physics-based machine learning Scientific machine learning is a rapidly growing area that seeks to combine traditional physics-based models with machine learnt models. In traditional physics-based modelling, you rely on expert knowledge to build the complete model (perhaps with some limited parameter fitting to experimental data); the machine learning approach replaces that expert knowledge with large quantities of data (that’s an over simplification but broadly correct). A key question is how to find the sweet spot in the middle whereby you make use of your expert knowledge but also include model features that emerge from the data....

2021-05-13