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.

翻译：深度神经网络在动力学系统预测中越来越受到关注，但分布外泛化和长期稳定性仍然是挑战。在本工作中，我们将动力学系统的领域参数视为数据生成过程中的变化因素。通过利用监督解耦和因果分解的思想，我们旨在将领域参数与生成模型潜在空间中的动力学分离。在实验中，我们在相空间和视频序列中建模动力学，并进行严格的OOD评估。结果表明，解耦VAE能够更好地适应训练数据中未出现的领域参数空间。同时，解耦可以改善视频序列中最先进模型的长期和分布外预测。