  Artificial Neural Networks (ANNs) have been successfully used in various
nuclear engineering applications, such as predicting reactor physics parameters
within reasonable time and with a high level of accuracy. Despite this success,
they cannot provide information about the model prediction uncertainties,
making it difficult to assess ANN prediction credibility, especially in
extrapolated domains. In this study, Deep Neural Networks (DNNs) are used to
predict the assembly axial neutron flux profiles in the SAFARI-1 research
reactor, with quantified uncertainties in the ANN predictions and extrapolation
to cycles not used in the training process. The training dataset consists of
copper-wire activation measurements, the axial measurement locations and the
measured control bank positions obtained from the reactor's historical cycles.
Uncertainty Quantification of the regular DNN models' predictions is performed
using Monte Carlo Dropout (MCD) and Bayesian Neural Networks solved by
Variational Inference (BNN VI). The regular DNNs, DNNs solved with MCD and BNN
VI results agree very well among each other as well as with the new measured
dataset not used in the training process, thus indicating good prediction and
generalization capability. The uncertainty bands produced by MCD and BNN VI
agree very well, and in general, they can fully envelop the noisy measurement
data points. The developed ANNs are useful in supporting the experimental
measurements campaign and neutronics code Verification and Validation (V&V).
