PhD project: Optimisation of outcomes in turbulent fluid equations
Project description
In many physical systems of interest, the vast range of temporal and spatial scales involved leads to difficulties for theory and for computation. Often (even heroic) computations are carried out at unrealistic parameters and inferences are made about the behaviour of the real system by examining scaling laws and balances. Examples include the behaviour of turbulent fluid systems and electrically conducting fluids and plasmas.
An interesting theoretical problem is how to maintain a desired behaviour (for example a specific balance of forces) of the system as the parameters are varied to more realistic values? This is clearly an optimisation problem and progress can be made by using machine learning and so-called differentiable codes. This project will develop strategies for computing such optimal paths numerically. These strategies will be benchmarked on simple systems of ordinary and partial differential equations. Subsequently, the developed methods will be applied to more extreme and turbulent flows relevant to a variety of physical processes.
Project supervisors
- Prof Steve Tobias (School of Physics & Astronomy, University of Edinburgh)
- Dr Calum Skene (School of Physics & Astronomy, University of Edinburgh)
The project supervisors welcome informal enquiries about this project.
Find out more about this research area
The links below summarise our research in the area(s) relevant to this project:
- Find out more about Statistical Physics and Complexity.
- Find out more about the Institute for Condensed Matter and Complex Systems.
What next?
- Find out how to apply for our PhD degrees.
- Find out about fees and funding and studentship opportunities.
- View and complete the application form (on the main University website).
- Find out how to contact us for more information.