A semi-supervised framework for computational fluid dynamics prediction

Abstract

Data-driven deep learning approach heavily relies on the diversity and quantity of data. Acquiring data in the computational fluid dynamics (CFD) domain is a time and computationally intensive process. This paper proposes a semi-supervised learning method called discriminative regression fitters (DRF) for aerodynamic prediction of airfoils. DRF utilizes neural networks’ memory property to dynamically divide pseudo-labeled data into easy and difficult subsets using a model of Gaussian distribution. The method classifies unlabeled data based on loss and updates the pseudo-labeled data, improving the model’s generalization capability. Experiments on airfoil regression task datasets show that DRF achieves similar or better prediction accuracy than fully supervised approaches. It reduces data acquisition time by 70%. Ablation studies and qualitative results verify the effectiveness of DRF. The surrogate model obtained from DRF is extended to airfoil optimization, demonstrating its practicality. DRF provides a promising direction for improving the regression task while reducing the reliance on large amounts of CFD data.