Decoder Decomposition for the Analysis of the Latent Space of Nonlinear Autoencoders With Wind-Tunnel Experimental Data

Understand

Turbulent flows are chaotic and multi-scale dynamical systems, which have large numbers of degrees of freedom.

• Turbulent flows, however, can be modelled with a smaller number of degrees of freedom when using the appropriate coordinate system, which is the goal of dimensionality reduction via nonlinear autoencoders.
• Autoencoders are expressive tools, but they are difficult to interpret.
• The goal of this paper is to propose a method to aid the interpretability of autoencoders.