Predicting turbulence and transport in magnetically confined plasmas is one of the major challenges in developing fusion energy. High-fidelity simulations based on nonlinear gyrokinetic theory can accurately model turbulent behaviour in tokamaks but are extremely computationally expensive, often requiring hundreds of thousands of CPU-hours for a single run.
Supervisors
| Name | Email address | Organisation(s) |
|---|---|---|
| Francesco Tudisco | School of Mathematics | |
| Harry Dudding | UKAEA |
To make large-scale predictive modelling feasible, simplified, lower-fidelity models such as linear gyrokinetics or gyro-fluid approximations are used, but these come at the cost of reduced accuracy.
This PhD project explores how machine learning can bridge these fidelity levels, combining the accuracy of high-fidelity models with the efficiency of reduced ones. The work will involve comparing and integrating different turbulence models to identify where simplified approaches remain valid, and developing machine learning-based correction models to improve their predictions. The project will also investigate whether machine learning can reconstruct high-resolution plasma behaviour from lower-resolution simulations, guided by physical insight into nonlinear interactions. In addition, we will explore how recent advances in large-scale deep generative modelling, including diffusion-based and autoregressive architectures, can be adapted to accelerate PDE solvers and plasma turbulence simulations.
