  This work aims to improve generalization and interpretability of dynamical
systems by recovering the underlying lower-dimensional latent states and their
time evolutions. Previous work on disentangled representation learning within
the realm of dynamical systems focused on the latent states, possibly with
linear transition approximations. As such, they cannot identify nonlinear
transition dynamics, and hence fail to reliably predict complex future
behavior. Inspired by the advances in nonlinear ICA, we propose a state-space
modeling framework in which we can identify not just the latent states but also
the unknown transition function that maps the past states to the present. We
introduce a practical algorithm based on variational auto-encoders and
empirically demonstrate in realistic synthetic settings that we can (i) recover
latent state dynamics with high accuracy, (ii) correspondingly achieve high
future prediction accuracy, and (iii) adapt fast to new environments.
