Minisymposium Presentation

Neural surrogate models of physics simulators are emerging ubiquitously in the Fusion community to satisfy the pressing need of fast optimisation tasks and flight simulator applications. However, gathering the training sets for these surrogates can be very expensive, and storing the data long-term may be impossible. In this talk I will demonstrate methodologies to obtain performing surrogate models at a significantly lower cost in terms of training data, with applications to 0D and 2D datasets and to a reactor-relevant streaming scenario.