Schematic overview of the cINN. During training, the cINN learns to encode all information about the physical parameters x in the latent variables z (while enforcing that they follow a Gaussian distribution) that is not contained in the observations y. At prediction time, conditioned on the new observation y, the cINN then transforms the known prior distribution p(z) to x-space to retrieve the posterior distribution p(x|y).