The goal of this work is to develop accurate Machine Learning (ML) models for
predicting the assembly axial neutron flux profiles in the SAFARI-1 research
reactor, trained by measurement data from historical cycles. The data-driven
nature of ML models makes them susceptible to uncertainties which are
introduced by sources such as noise in training data, incomplete coverage of
the domain, extrapolation and imperfect model architectures. To this end, we
also aim at quantifying the approximation uncertainties of the ML model
predictions. Previous work using Deep Neural Networks (DNNs) has been
successful for fuel assemblies in SAFARI-1, however, not as accurate for
control follower assemblies. The aim of this work is to improve the ML models
for the control assemblies by a combination of supervised and unsupervised ML
algorithms. The $k$-means and Affinity Propagation unsupervised ML algorithms
are employed to identify clusters in the set of the measured axial neutron flux
profiles. Then, regression-based supervised ML models using DNN (with
prediction uncertainties quantified with Monte Carlo dropout) and Gaussian
Process (GP) are trained for different clusters and the prediction uncertainty
is estimated. It was found that applying the proposed procedure improves the
prediction accuracy for the control assemblies and reduces the prediction
uncertainty. Flux shapes predicted by DNN and GP are very close, and the
overall accuracy became comparable to the fuel assemblies. The prediction
uncertainty is however smaller for GP models.

本研究旨在利用机器学习模型准确预测 SAFARI-1 研究反应堆中装配件轴向中子通量分布，通过使用历史周期的测量数据进行训练，同时量化模型预测的近似不确定性，并通过组合监督和无监督机器学习算法改进控制装配件的预测精度和降低预测不确定性。

聚类与不确定性分析改进基于机器学习的 SAFARI-1 控制从属组件轴向中子通量剖面预测

Clustering and Uncertainty Analysis to Improve the Machine  Learning-based Predictions of SAFARI-1 Control Follower Assembly Axial  Neutron Flux Profiles