  Multi-fidelity models are becoming more prevalent in engineering,
particularly in aerospace, as they combine both the computational efficiency of
low-fidelity models with the high accuracy of higher-fidelity simulations.
Various state-of-the-art techniques exist for fusing data from different
fidelity sources, including Co-Kriging and transfer learning in neural
networks. This paper aims to implement a multi-fidelity Bayesian neural network
model that applies transfer learning to fuse data generated by models at
different fidelities. Bayesian neural networks use probability distributions
over network weights, enabling them to provide predictions along with estimates
of their confidence. This approach harnesses the predictive and data fusion
capabilities of neural networks while also quantifying uncertainty. The results
demonstrate that the multi-fidelity Bayesian model outperforms the
state-of-the-art Co-Kriging in terms of overall accuracy and robustness on
unseen data.
