Deep networks have become increasingly of interest in dynamical system prediction, but generalization remains elusive. In this work, we consider the physical parameters of ODEs as factors of variation of the data generating process. By leveraging ideas from supervised disentanglement in VAEs, we aim to separate the ODE parameters from the dynamics in the latent space. Experiments show that supervised disentanglement allows VAEs to capture the variability in the dynamics and extrapolate better to ODE parameter spaces that were not present in the training data.

翻译：深网络对动态系统预测的兴趣日益浓厚,但一般化仍然难以实现。 在这项工作中,我们认为脱氧核糖核酸的物理参数是数据生成过程变化的因素。 通过利用VAEs中受监管的分解观点,我们的目标是将脱氧核糖核酸参数与潜在空间的动态分开。实验显示,由监管的分解使得脱氧核糖核酸能够捕捉动态的变异性,并更好地推断出在培训数据中不存在的脱氧核糖核酸参数空间。