Estimation of hypersonic vehicle weight using Physics-Informed neural network supported by knowledge based engineering

Abstract

Design space exploration is usually required during the conceptual design of hypersonic vehicle. Artificial neural network (ANN) is widely used for issues of design space exploration. However, gaps such as the low prediction accuracy of ANN with small training data and inefficiency of data generation are still unaddressed. In this paper, for investigation of the variation of the maximum takeoff weight (MTOW) of hypersonic vehicles as the configuration changes, a method combing physics-informed neural network (PINN) and knowledge based engineering (KBE) is developed to study the issue. In this method, PINN is used as the predictor of MTOW, the prior knowledge of engineers is able to be embedded into the loss function and guide the training of PINN. Compared with that of traditional neural network, the prediction accuracy of PINN is higher especially with small training data. Moreover, the KBE is used to guide the design process of generating training data and the efficiency of data generation can be improved. In order to account for the validity of the proposed method, the MTOW prediction of hypersonic vehicles with different wing configurations has been investigated. The results showed that the prediction accuracy of neural network can be improved with small training data.