The use of deep neural networks for modelling system dynamics is increasingly popular, but long-term prediction accuracy and out-of-distribution generalization still present challenges. In this work, we treat the parameters of dynamical systems as factors of variation in the data and use the ground-truth values of those parameters to disentangle the representations generative models. Our experiments in phase-space and observation-space dynamics indicate that supervision can effectively produce d…