## 1 Introduction
However, the harmonic balance method has the problem of converting time and frequency domains. Therefore, a time spectrum method (TSM) was further proposed, taking into account the periodic characteristics of the flow. Compared with the harmonic balance method, the operation process of the TSM is carried out in the time domain, avoiding the conversion between the time domain and frequency domain. TSM has been widely applied in various engineering problems of periodic unsteady flow, such as helicopter-rotor-motion and vortex-shedding problems [6]. Overall, the current means of calculating dynamic derivatives with CFD mainly includes the two methods of time domain and frequency domain. The time domain method is simple, but the identification efficiency of dynamic derivatives is low due to a large number of unsteady aerodynamic force calculations. Especially for complex, full aircraft, the huge numbers of grids makes the acquisition of dynamic derivatives seriously lag behind the design of the configuration scheme. Although the computational complexity of frequency domain methods is small, on the one hand, the solver needs to be changed, and on the other hand, the stability of its flow field reconstruction is not high, and the accuracy also needs to be further explored. Overall, there is still a lack of efficient and accurate dynamic derivative simulation methods. With the development of artificial intelligence, researchers have conducted a lot of research on aerodynamic force identification using neural networks. Among them, [PERSON] et al. [7] established an unsteady aerodynamic model of aircraft at high angles of attack based on an improved BP neural network method; [PERSON] et al. [8] used neural network methods to model unsteady aerodynamic forces at high angles of attack, and compared the accuracy with dynamic derivative models and polynomial models. The results showed that the aerodynamic modeling method based on neural networks has high accuracy and adaptability. However, the above research methods are all based on Back Propagation (BP) neural networks, which have certain limitations in processing time series data such as unsteady aerodynamics. For the above problems, we innovatively propose a new method of \"Modeling with a small number of samples-dynamic derivatives identification\". We trained a long short-termmemory network (LSTM) using a small number of sample data, and built an intelligent model of unsteady aerodynamic force. A particle swarm optimization (PSO) algorithm is used to optimize the hyper-parameters of the neural network to improve the training efficiency and accuracy of the model. By using this model, we predict the unsteady aerodynamic force of unknown states, and the dynamic derivatives are obtained using the least-squares method. By replacing data calculation with models, the effectiveness of obtaining dynamic derivatives is greatly improved. The optimization strategy of the intelligent modeling process also ensures the accuracy of dynamic derivatives prediction. Compared to the method based on the BP neural network mentioned above, the accuracy is improved by 4-5 times, and the required training data are fewer.