Machine learning applications for predicting the pedestal in tokamak plasmas

Sammanfattning: Magnetic confinement fusion is a field of research that strives to develop an environmental friendly energy source to assist in powering our society. By confining a plasma with magnetic fields, conditions that enable nuclear fusion can be achieved. However, gaining a high efficiency has proven to be a challenging task. In the 1980s, it was discovered that steep temperature and density gradients are formed near the plasma edge when the external heating passes a certain threshold leading to an increased energy and particle confinement. The region with steep gradients at the edge is referred to as the pedestal. As of today the formation and behaviour of the pedestal is still not fully understood from a theoretical standpoint. However, the enhanced performance of plasmas with a developed pedestal is routinely exploited in current fusion experiments, and is a key element in extrapolating to future devices. The purpose of this thesis is to explore machine learning methodologies to help improve the understanding and predictive capabilities of the pedestal. Specifically, a neural network for predicting pedestal characteristics has been developed and integrated with core transport models. Additionally, another neural network has been developed to enhance the temporal resolution of the main diagnostics used to analyse the pedestal. The thesis incorporates additional machine learning applications for plasma physics that extend beyond a specific focus on the pedestal.