Abstract: It is of great importance to establish a reliable flight dynamic model based on aerodynamic parameter identification in the process of aircraft design. Traditional engineering methods for aerodynamic parameter identification, such as the maximum likelihood method etc., require a reasonable dynamic model and initial values for the parameters to be identified. The identification of aerodynamic parameters based on traditional neural networks has no requirement for the flight dynamic model and parameter initialization. In this study, an aerodynamic parameter identification method based on physics informed neural network (PINN) is proposed, which can directly identify aerodynamic parameters by incorporating the flight dynamic model as a regularization term into the loss function. This method can significantly reduce the required training data and improve the modeling accuracy. Verification results for the flight simulation data show that the maximum relative errors of the PINN based method on the longitudinal flight state space model identification with no noise and 2% noise are 1.80% and 4.64%, respectively. It suggests that the PINN based aerodynamic parameter identification method is feasible and has the generalization capacity to noisy flight data.