  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.
