针对传统深度学习模型在三维飞行器超声速条件下反设计中的收敛困难，提出了一种基于先验预测模型引导的生成式门控去噪扩散概率模型（DDPM）。该模型通过门控机制，集成了气动性能预测与飞行器外形反设计的功能，旨在提高预测精度和反设计效率，同时克服传统模型在处理高度非线性问题时的训练挑战。通过构建深度神经网络（DNN）作为先验预测模型，以获取初步的气动性能预测和几何外形估计。基于这些初步结果，门控DDPM结合DNN的预测信息，利用扩散过程和逆扩散过程来生成设计结果。扩散过程逐步添加高斯噪声，逆扩散过程则恢复数据分布，这一机制提升了模型在复杂气动设计任务中的收敛性和预测准确性。研究基于轴对称飞行器数据集，验证了门控DDPM在气动性能预测和反设计中的有效性。相比先验模型，门控DDPM在反设计任务中能够将大多数气动参数的相对误差控制在0.75%以内，显著优于DNN模型。结果表明，所提方法有效提高了飞行器设计中的气动性能预测和外形反设计的精度。

A generative gated denoising diffusion probabilistic model (DDPM) based on a priori prediction model guidance is proposed to address the convergence challenges faced by traditional deep learning models in the inverse design of three-dimensional aircraft under supersonic conditions. This model integrates aerodynamic performance prediction and aircraft shape inverse design through a gating mechanism, aiming to enhance both prediction accuracy and design efficiency while overcoming the training difficulties encountered in handling highly nonlinear problems with traditional models. A deep neural network (DNN) serves as an a priori prediction model to obtain preliminary aerodynamic performance predictions and geometric shape estimates. Based on these initial results, the gated DDPM combines the predictive information from the DNN and employs diffusion and reverse diffusion processes to generate design outcomes. In this process, Gaussian noise is gradually added during the diffusion phase, and the reverse diffusion phase restores the data distribution. This mechanism enhances both convergence and predictive accuracy in complex aerodynamic design tasks. The effectiveness of the gated DDPM is validated using an axisymmetric aircraft dataset. Compared to the prior model, the gated DDPM can control the relative error of most aerodynamic parameters within 0.75% in inverse design tasks, significantly outperforming the DNN model. The results demonstrate that the proposed method effectively enhances the accuracy of aerodynamic performance prediction and shape inverse design in aircraft design.