Physically Guided Neural Network Based on Transfer Learning (TL-PGNN) for Hypersonic Heat Flux Prediction (Communications in Computational Physics, 2024)
Hypersonic vehicles create high-temperature environments, thus causingair molecules to undergo chemical reactions. As a result, the nonlinearity of the mapping relationship between heat flux and inflow condition is strengthened. Accuratelypredicting the heat flux of real gases using machine learning methods relies on anamount of training data of real gas, but the computational cost is often unacceptable.To solve the issue stated, we propose a novel neural network named Physically GuidedNeural Network based on Transfer Learning (TL-PGNN). We design a new networkarchitecture to depict physical laws and use heat flow data of ideal gases to enhancethe network’s capability to depict heat flow from only a few real gas samples. Experiments of sphere demonstrate that, compared to ordinary DNN and PGNN, applyingTL-PGNN decreases the mean L1 error by 72.66% and 24.53% on the test set, respectively.