In this paper, a novel model is presented for reconstructing unsteady periodic fields of velocity vector and pressure scalar over an oscillating foil. This data-driven method based on convolutional neural network can be utilized to accomplish two objections: fields reconstruction from limited measurements and transient aerodynamic characteristics prediction. The verification results of an oscillating foil under low Reynolds number show that this method can accurately reconstruct all the fields only by limited pressure information at probes on the foil surface. The evaluation on aerodynamic characteristics prediction illustrates that our model outperforms four classical machine learning methods. Meanwhile, a well-trained CNN model can almost achieve real-time flow field prediction by leveraging the GPU acceleration. Finally, the exploration of the robustness for the CNN model is conducted on several aspects, including training size, probe layouts, probe numbers and measurement noises.

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