南京航空航天大学学报（英文版）2026，Vol.43Issue(4)：592-613,22.DOI:10.16356/j.1005-1120.2026.04.008
基于混合CNN与Chebyshev-Kolmogorov-Arnold网络的高精度翼型压力分布预测模型
A High-Precision Airfoil Pressure Distribution Prediction Model Based on Hybrid CNN and Chebyshev-Kolmogorov-Arnold Network
摘要
Abstract
To address the high computational costs of traditional computational fluid dynamics(CFD)in aerodynamic optimization and the limitations of conventional deep learning models,such as slow convergence and insufficient accuracy in capturing complex flow physics,a novel framework integrating convolutional neural network(CNN)and Chebyshev-Kolmogorov-Arnold network(Cheby-KAN)is proposed for 2D airfoil pressure distribution prediction.The framework employs a CNN-based geometry encoder to automatically extract high-dimensional features from signed distance field(SDF)and map them into low-dimensional latent vectors,thereby bypassing traditional manual parameterization.In the regression module,shifted Chebyshev polynomials are introduced as basis functions within the KAN architecture(Cheby-KAN).Leveraging their orthogonality and superior approximation properties,Cheby-KAN effectively mitigates the Runge phenomenon and enhances numerical stability when resolving high-gradient pressure patterns.Experimental results on a dataset comprising 89 million data points demonstrate that Cheby-KAN achieves a 60%reduction in training time compared to conventional multi-layer perceptron(MLP),with a 92%decrease in training mean square error(MSE).Furthermore,the model exhibits exceptional generalization in"zero-shot"tests on unseen airfoils,maintaining a mean absolute error(MAE)of 2.1×10-3,which underscores its robustness as a high-fidelity surrogate model for advanced aerodynamic design.关键词
翼型压力分布/深度学习/卷积神经网络/切比雪夫多项式/柯尔莫哥洛夫-阿诺德网络/气动预测Key words
airfoil pressure distribution/deep learning/convolutional neural network (CNN)/Chebyshev polynomials/Kolmogorov-Arnold network (KAN)/aerodynamic prediction分类
航空航天引用本文复制引用
陈耀鸿,张路驰,邓一菊,于彦泽,李响,焦仁山..基于混合CNN与Chebyshev-Kolmogorov-Arnold网络的高精度翼型压力分布预测模型[J].南京航空航天大学学报（英文版）,2026,43(4):592-613,22.基金项目
This work was supported by the the Yangzhou Science and Technology Plan Project(No.YZ2025232). （No.YZ2025232）
