The objective of this webinar is to present the prototype of a machine learning tool to enable the exploration, analysis, and interpretation of the outputs of large-volume cosmological simulations using Representation Learning techniques. The tool efficiently learns a low-dimensional representation of the structure of simulated galaxies in arbitrary physical components, uncovering their intrinsic structural distribution. It also provides an interactive hierarchical visualization of the entire simulation and its compact representation, and scales to arbitrarily large simulations beyond the Exascale era.