Interpretable machine learning applied to fusion plasmas

Doctoral thesis, 2026

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, which is why fusion research is still at the experimental stage. On another scientific front, machine learning (ML) and artificial intelligence (AI) are advancing with tremendous momentum, and are rapidly being integrated into fusion research. However, the black-box aspect of many popular ML architectures inhibits our ability to interpret what the models have learned during training. Ideally, we would like to understand ML models deployed in fusion research to gain knowledge that may prove useful for advancing the field.

This thesis focuses on exploring interpretable ML methods in fusion research. As a consequence, an interpretable framework called NeuralBranch has been developed, which has been applied to two different use cases in fusion. The main application in this thesis relates to the so-called pedestal, which has significance for the energy confinement in fusion experiments. The other, more secondary application in this thesis, relates to the growth rate of plasma instabilities that contribute to heat and particle transport. In summary, the interpretability of the machine learning models deployed reveals intricate parameter relationships in both these applications, beyond what previous traditional data-fitting approaches have been able to reveal.