EuroPED-NN: Uncertainty aware surrogate model
A. Panera Alvarez A. Ho A. Jarvinen S. Saarelma S. Wiesen JET Contributors
This work successfully generates uncertainty aware surrogate models, via the
Bayesian neural network with noise contrastive prior (BNN-NCP) technique, of
the EuroPED plasma pedestal model using data from the JET-ILW pedestal database
and subsequent model evaluations. All this conform EuroPED-NN. The BNN-NCP
technique is proven to be a good fit for uncertainty aware surrogate models,
matching the output results as a regular neural network, providing prediction's
confidence as uncertainties, and highlighting the out of distribution (OOD)
regions using surrogate model uncertainties. This provides critical insights
into model robustness and reliability. EuroPED-NN has been physically
validated, first, analyzing electron density
$n_e\!\left(\psi_{\text{pol}}=0.94\right)$ with respect to increasing plasma
current, $I_p$, and second, validating the $\Delta-\beta_{p,ped}$ relation
associated with the EuroPED model. Affirming the robustness of the underlying
physics learned by the surrogate model.