Physically Organized Latent Spaces in Unsupervised Autoencoders: Evidence from Aerodynamic Databases

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 variabl