Towards validated higher-fidelity AI models in fusion exhaust

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved modelling allowing more flexible integration of physics models in the light of extrapolations towards future fusion devices. The project encompasses various research objectives: a) developments of surrogate model predictors for power & particle exhaust in fusion power plants; b) assessments of surrogate models for time-dependent phenomena in the plasma-edge; c) feasibility studies of micro-macro model discovery for plasma-facing components surface morphology & durability; and d) enhancements of pedestal models & databases through interpolators and generators exploiting uncertainty quantification. Presented results demonstrate useful applications for machine-learning and artificial intelligence in fusion exhaust modeling schemes, enabling an unprecedented combination of both fast and accurate simulation. Validated surrogate models are the foundations for accurate integrated models, such as for digital-twins or high-fidelity plasma simulators. Machine learning based techniques to improve the model fidelity, such as transfer or active learning, will be discussed.