A convolutional autoencoder is trained using a database of airfoil aerodynamic simulations and assessed in terms of overall accuracy and interpretability. The goal is to predict the stall and to investigate the ability of the autoencoder to distinguish between the linear and non-linear response of the airfoil pressure distribution to changes in the angle of attack. After a sensitivity analysis on the learning infrastructure, we investigate the latent space identified by the autoencoder targeting extreme compression rates, i.e. very low-dimensional reconstructions. We also propose a strategy to use the decoder to generate new synthetic airfoil geometries and aerodynamic solutions by interpolation and extrapolation in the latent representation learned by the autoencoder.

翻译：在对学习基础设施进行敏感性分析后,我们调查了自动编码器针对极端压缩速率(即非常低的维度重建)所查明的潜在空间。我们还提出了一项战略,即利用拆解器生成新的合成气动油气流地貌和气动解决方案,通过对自动编码器所了解的潜表层进行内插和外推。