The use of ML in engineering has grown steadily to support a wide array of applications. Among these methods, deep neural networks have been widely adopted due to their performance and accessibility, but they require large, high-quality datasets. Experimental data are often sparse, noisy, or insufficient to build resilient data-driven models. Transfer learning, which leverages relevant data-abundant source domains to assist learning in data-scarce target domains, has shown efficacy. Parameter transfer, where pretrained weights are reused, is common but degrades under large domain shifts. Domain-adversarial neural networks (DANNs) help address this issue by learning domain-invariant representations, thereby improving transfer under greater domain shifts in a semi-supervised setting. However, DANNs can be unstable during training and lack a native means for uncertainty quantification. This study introduces a fully-supervised three-stage framework, the staged Bayesian domain-adversarial neural network (staged B-DANN), that combines parameter transfer and shared latent space adaptation. In Stage 1, a deterministic feature extractor is trained on the source domain. This feature extractor is then adversarially refined using a DANN in Stage 2. In Stage 3, a Bayesian neural network is built on the adapted feature extractor for fine-tuning on the target domain to handle conditional shifts and yield calibrated uncertainty estimates. This staged B-DANN approach was first validated on a synthetic benchmark, where it was shown to significantly outperform standard transfer techniques. It was then applied to the task of predicting critical heat flux in rectangular channels, leveraging data from tube experiments as the source domain. The results of this study show that the staged B-DANN method can improve predictive accuracy and generalization, potentially assisting other domains in nuclear engineering.

翻译：机器学习在工程领域的应用持续增长，以支持广泛的应用场景。在这些方法中，深度神经网络因其卓越的性能和易用性而被广泛采用，但它们需要大规模、高质量的数据集。实验数据往往稀疏、噪声大或不足以构建稳健的数据驱动模型。迁移学习通过利用数据丰富的相关源域来辅助数据稀缺目标域的学习，已显示出其有效性。参数迁移（即重用预训练权重）是常见方法，但在领域差异较大时性能会下降。领域对抗神经网络通过学习领域不变表征，有助于解决这一问题，从而在半监督设置下提升较大领域差异时的迁移效果。然而，DANNs在训练过程中可能不稳定，且缺乏固有的不确定性量化手段。本研究提出了一种全监督的三阶段框架——分阶段贝叶斯领域对抗神经网络，该框架结合了参数迁移和共享潜在空间适应。第一阶段，在源域上训练一个确定性特征提取器。第二阶段，使用DANN对该特征提取器进行对抗性精炼。第三阶段，在适应后的特征提取器上构建贝叶斯神经网络，并在目标域上进行微调，以处理条件偏移并产生校准的不确定性估计。该分阶段B-DANN方法首先在合成基准测试中得到验证，结果表明其显著优于标准迁移技术。随后，该方法被应用于预测矩形通道内临界热通量的任务，利用管式实验数据作为源域。本研究结果表明，分阶段B-DANN方法能够提高预测准确性和泛化能力，有望为核工程等其他领域提供助力。