Abstract: We investigate the possibility of learning the representations of cosmological multifield dataset from the CAMELS project. We train a very deep variational encoder on images which comprise three channels, namely gas density (Mgas), neutral hydrogen density (HI), and magnetic field amplitudes (B). The clustering of the images in feature space with respect to some cosmological/astrophysical parameters (e.g. $\Omega_{\rm m}$) suggests that the generative model has learned latent space representations of the high dimensional inputs. We assess the quality of the latent codes by conducting a linear test on the extracted features, and find that a single dense layer is capable of recovering some of the parameters to a promising level of accuracy, especially the matter density whose prediction corresponds to a coefficient of determination $R^{2}$ = 0.93. Furthermore, results show that the generative model is able to produce images that exhibit statistical properties which are consistent with those of the training data, down to scales of $k\sim 4h/{\rm Mpc}.$