Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering. General reconstruction methods for recovering the field, especially the deep learning with more parameters and better representational ability, usually require large amounts of labeled data which is unaffordable. To solve the problem, this paper proposes Uncertainty Guided Ensemble Self-Training (UGE-ST), using plentiful unlabeled data to improve reconstruction performance. A novel self-training framework with the ensemble teacher and pretraining student designed to improve the accuracy of the pseudo-label and remedy the impact of noise is first proposed. On the other hand, uncertainty-guided learning is proposed to encourage the model to focus on the highly confident regions of pseudo-labels and mitigate the effects of wrong pseudo-labeling in self-training, improving the performance of the reconstruction model. Experiments include the pressure velocity field reconstruction of airfoil and the temperature field reconstruction of aircraft system indicate that our UGE-ST can save up to 90% of the data with the same accuracy as supervised learning.

翻译：从有限传感器中恢复全局精确的复杂物理场是航空航天工程中测量与控制的关键。通用物理场重建方法（特别是具有更多参数和更强表征能力的深度学习方法）通常需要大量标注数据，这在实践中难以承受。为解决此问题，本文提出不确定性引导集成自训练（UGE-ST），利用丰富的无标注数据提升重建性能。首先提出一种集成教师模型与预训练学生模型的新型自训练框架，以提高伪标签精度并减轻噪声影响。另一方面，引入不确定性引导学习机制，促使模型聚焦于伪标签中高置信度区域，缓解自训练中错误伪标签的影响，从而提升重建模型性能。包含翼型压力速度场重建与飞机系统温度场重建的实验表明，我们的UGE-ST在保持与监督学习相同精度的前提下，可节省高达90%的数据。