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.

翻译：本研究利用贝叶斯神经网络与噪声对比先验（BNN-NCP）技术，基于JET-ILW台基数据库和后续模型评估数据，成功生成了EuroPED等离子体台基模型的不确定性感知代理模型，统称为EuroPED-NN。BNN-NCP技术被证明完美适用于不确定性感知代理模型：其输出结果与常规神经网络匹配，以不确定性形式提供预测置信度，并通过代理模型不确定性突出分布外（OOD）区域。这为模型鲁棒性和可靠性提供了重要洞察。EuroPED-NN经过物理验证：首先分析了电子密度$n_e\!\left(\psi_{\text{pol}}=0.94\right)$随等离子体电流$I_p$增大的变化规律，其次验证了EuroPED模型相关的$\Delta-\beta_{p,ped}$关系，确认了代理模型所学底层物理机制的鲁棒性。