Fusion data science at infusion

The research unit Nuclear Fusion at Ghent University (UGent) is at the forefront of research on the development of fusion as a clean, safe and plentiful energy source. We employ modern techniques from Bayesian inference and machine learning to study the fusion plasma and the technology of fusion devices. This combines two of the most topical and challenging issues of our time: sustainable energy supply and data science.

 

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Research topics of the group infusion

Our research activities are situated in the field of fusion data science. Below is a selection of our current research topics.

Scaling laws explain dependencies of key plasma parameters and contribute to the design of new fusion devices. They are estimated from complex databases by means of sophisticated statistical techniques. Our group plays a leading role in this domain.

We use probabilistic methods for characterizing stochasticity in fusion plasmas, like plasma instabilities and fluctuations. The challenge is to determine the plasma properties and machine design parameters that influence the corresponding probability distributions, reflecting the underlying physics.

We use Bayesian probabilistic inference for integrated analysis of data from multiple sensors (diagnostics) in fusion devices. This type of "data fusion" allows more reliable measurement of plasma conditions with uncertainty estimates.

Europe is at the forefront of developing one of the most promising long-term energy options: fusion power.

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