Efficient pitching moment prediction for canard-controlled missiles via transfer learning-based deep learning

Aerodynamic design of aerospace vehicles often necessitates extensive Computational Fluid Dynamics (CFD) simulations, which are computationally expensive. To address this, we propose an efficient multilayer neural network framework to predict the pitching moment coefficient of canard-controlled missiles. A low-fidelity database of over 28,000 points was rapidly generated using Missile DATCOM and used to train an initial neural network with four hidden layers. The core of our methodology is an architectural transfer learning approach, where this pre-trained model initializes a high-fidelity network, significantly reducing the need for costly CFD data (using only 120 samples). The Levenberg-Marquardt algorithm’s hyperparameters were fine-tuned to optimize performance. The final model achieved a Root Mean Square Error (RMSE) of 0.0055 on a random test dataset. The model’s stability and generalization capability were further confirmed through 5-fold cross-validation, which demonstrated robust performance. This highly accurate and validated model provides a powerful surrogate tool for rapid design optimization based on trim angle of attack and geometric parameters.