This work is based on the recent feasibility study that is conducted in response to the recent challenge by GameChangers to devise digital technologies to support development, deployment, operation and decommissioning of Advanced Nuclear Technologies. There are 2 distinct objectives to this work.

The first objective would be to present a review of the state of the implementation of Artificial Intelligence (AI) in the nuclear industry today. This seeks to provide a background overview of the challenges faced by the industry from which the benefits and opportunities for AI would be discussed. The second objective would be to demonstrate the implementation of Artificial Intelligence tool through the use of Artificial Neural Networks along with Uncertainty Quantification tools to perform probabilistic prediction of material properties in nuclear reactors. The idea is to allow for the prediction to account for the inherent variability of the material properties as well as the uncertainty associated with the lack of information due to sparse data. To achieve this, Bayesian Neural Network is employed as the stochastic model for stochastic model updating. The purpose of which is to enhance the sparse data-set from which it will be used to train and validate the existing Artificial Neural Networks which have been developed.

The eventual research seeks to illustrate the potential and benefit of the implementation of AI to address a nuclear industrial challenge as highlighted in the review.