发表机构
Università di Napoli Federico II; Stanford University（那不勒斯费德里科二世大学; 斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究证明仅通过重建训练的无监督自编码器能自发组织潜在空间以反映空气动力学物理规律，且该组织可复现并线性编码物理参数。
AI 中文摘要
利用复杂度逐步增加的空气动力学数据库——从线性解析理论到非线性分离的RANS流动——我们证明，仅通过重建训练的自编码器能够自发地根据物理上有意义的变量和空气动力学定律组织潜在表示。尽管潜在坐标本身在随机初始化下会变化，但潜在的物理组织是可复现的，甚至在定量上也是如此。此外，我们展示了通过顺序训练的无监督自编码器获得的翼型流动的三维潜在空间表示，其中无粘流场与边界层效应解耦，第三个潜在变量与雷诺数和空气动力学阻力强相关。交叉验证的回归和仿射对齐测量表明，物理参数和响应被强且线性地编码在学习到的坐标中，且在不同独立训练中保持一致。
英文摘要
Using aerodynamic databases of progressively increasing complexity - from linear analytical theory to nonlinear separated RANS flows - we demonstrate that an autoencoder, trained solely by reconstruction, can spontaneously organize the latent representation according to physically meaningful variables and aerodynamic laws. Although the latent coordinates themselves vary under random initialization, the underlying physical organization is reproducible, even quantitatively. In addition, we present a three-dimensional latent space representation of the airfoil flow obtained by an unsupervised autoencoder with sequential training in which the inviscid field is decoupled from the boundary layer effects with the third latent variable strongly correlated with the Reynolds number and aerodynamic drag. Cross-validated regression and affine-alignment measures show that physical parameters and responses are strongly and linearly encoded in the learned coordinates, consistently across independent trainings.
