  Deep neural networks have become increasingly of interest in dynamical system
prediction, but out-of-distribution generalization and long-term stability
still remains challenging. In this work, we treat the domain parameters of
dynamical systems as factors of variation of the data generating process. By
leveraging ideas from supervised disentanglement and causal factorization, we
aim to separate the domain parameters from the dynamics in the latent space of
generative models. In our experiments we model dynamics both in phase space and
in video sequences and conduct rigorous OOD evaluations. Results indicate that
disentangled VAEs adapt better to domain parameters spaces that were not
present in the training data. At the same time, disentanglement can improve the
long-term and out-of-distribution predictions of state-of-the-art models in
video sequences.
