The study on flow around a hydrofoil has been performed using various methods experimentally and numerically. Here we use a purely data-driven model using deep neural networks (DNNs) to reconstruct the flow fields. Its results are also compared with that obtained by traditional methods. The datasets of flow fields around a static hydrofoil obtained from computational fluid dynamics (CFD) are used for training the DNNs and then the trained data-driven model is utilized to make reconstructions and predictions. 9 different physics informed loss functions, which contain the prediction error and the error measuring the violation of the conservation law, are proposed to train the parameters of DNNs. Effects of the proposed loss functions on reconstruction of velocity field and pressure field are analyzed. Lift and drag forces predicted during the training time are also analyzed. However, the data-driven model based on DNNs with the optimum loss function works well in the interpolation but fails in the extrapolation. The reasons causing the errors are discussed.