Data-driven methods for predicting aerodynamic coefficients of arbitrary shapes have received considerable attention due to their flexibility and scalability. This paper introduces a deep-learning framework based on a deep residual neural network and K-fold cross-validation for fast and accurate prediction of aerodynamic coefficients of a three-dimensional cone with shape changes due to ablation. The proposed neural network model is trained to learn the underlying relationship between shape transformations and the corresponding changes in aerodynamic coefficients. The shape transformations due to ablation are expressed as the difference between the nominal and ablated cones, measured in units of mesh coordinates. Multiple (K) models constructed based on the training process are combined to reduce the prediction variance effectively. The resulting ensemble model shows an improved prediction performance for various aerodynamic coefficients. To validate our methodology, we compare our model with the generic Multilayer Perceptron (MLP) with a varying number of neurons and the Gaussian process (GP) regression. The test results indicate that the proposed model predicts the aerodynamic coefficients more accurately than the baseline model (MLP/GP), indicating an improved generalization to unseen data.

由于其灵活性和可扩展性，用于预测任意形状的空气动力学系数的数据驱动方法受到了相当大的关注。本文介绍了一种基于深度残差神经网络和 K 折交叉验证的深度学习框架，用于快速准确地预测因烧蚀而发生形状变化的三维锥体的空气动力学系数。所提出的神经网络模型经过训练以了解形状变换与空气动力学系数的相应变化之间的潜在关系。由于烧蚀引起的形状变换表示为标称锥体和烧蚀锥体之间的差异，以网格坐标为单位测量。多个 ( K) 结合基于训练过程构建的模型，有效降低预测方差。由此产生的集合模型显示了对各种空气动力学系数的改进预测性能。为了验证我们的方法，我们将我们的模型与具有不同数量神经元和高斯过程 (GP) 回归的通用多层感知器 (MLP) 进行比较。测试结果表明，所提出的模型比基线模型 (MLP/GP) 更准确地预测空气动力学系数，表明改进了对未见数据的泛化。