A multi-task learning method for large discrepant aerodynamic data

Compared to traditional aerodynamic modeling methods, the deep-learning-based aerodynamic modeling has higher modeling efficiency and precision. However, previous deep learning methods with fully connected network (FCN) or convolutional neural network (CNN) ignored the influence of input data discrepancies. The shape characteristic parameters and flow state parameters of aircraft are greatly discrepant with different types, which will cause different degrees of impacts on the predicted results. When these two types of parameters are used to predict aerodynamic characteristics together, if we ignore the data discrepancy between them, the accuracy of prediction will be lost. Inspired by the multi-task learning method, we propose Large-Discrepancy Multi-task Learning Network(LD-MTL). Our method firstly divides the dataset into multiple tasks, and then splits the whole learning network into multiple clusters to learn relevant knowledge of the predicted aerodynamic performance according to different tasks. Finally, the relevant knowledge learned from individual clusters are then fused into the final prediction result. Through comparative tests, it is shown that our method can better reflect and quantify the influence of data discrepancies and have a higher prediction accuracy when modeling with largely discrepant aerodynamic data.