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.

翻译：多保真度模型在工程领域，特别是航空航天领域正变得越来越普遍，因为它们兼具低保真度模型的计算效率与高保真度仿真的高精度。目前存在多种融合不同保真度源数据的先进技术，包括协同克里金法（Co-Kriging）和神经网络中的迁移学习。本文旨在实现一种多保真度贝叶斯神经网络模型，该模型应用迁移学习来融合不同保真度模型生成的数据。贝叶斯神经网络通过对网络权重施加概率分布，使其能够提供预测结果并估计其置信度。该方法利用了神经网络的预测和数据融合能力，同时还能量化不确定性。结果表明，在未见数据的整体精度和鲁棒性方面，多保真度贝叶斯模型优于当前最先进的协同克里金法。