  The quality of datasets is one of the key factors that affect the accuracy of
aerodynamic data models. For example, in the uniformly sampled Burgers'
dataset, the insufficient high-speed data is overwhelmed by massive low-speed
data. Predicting high-speed data is more difficult than predicting low-speed
data, owing to that the number of high-speed data is limited, i.e. the quality
of the Burgers' dataset is not satisfactory. To improve the quality of
datasets, traditional methods usually employ the data resampling technology to
produce enough data for the insufficient parts in the original datasets before
modeling, which increases computational costs. Recently, the mixtures of
experts have been used in natural language processing to deal with different
parts of sentences, which provides a solution for eliminating the need for data
resampling in aerodynamic data modeling. Motivated by this, we propose the
multi-task learning (MTL), a datasets quality-adaptive learning scheme, which
combines task allocation and aerodynamic characteristics learning together to
disperse the pressure of the entire learning task. The task allocation divides
a whole learning task into several independent subtasks, while the aerodynamic
characteristics learning learns these subtasks simultaneously to achieve better
precision. Two experiments with poor quality datasets are conducted to verify
the data quality-adaptivity of the MTL to datasets. The results show than the
MTL is more accurate than FCNs and GANs in poor quality datasets.
