  Modern machine learning will allow for simulation-based inference from
reionization-era 21cm observations at the Square Kilometre Array. Our framework
combines a convolutional summary network and a conditional invertible network
through a physics-inspired latent representation. It allows for an optimal and
extremely fast determination of the posteriors of astrophysical and
cosmological parameters. The sensitivity to non-Gaussian information makes our
method a promising alternative to the established power spectra.
