Learning Fine Scale Dynamics from Coarse Observations via Inner Recurrence. (arXiv:2206.01807v1 [cs.LG])

Recent work has focused on data-driven learning of the evolution of unknown
systems via deep neural networks (DNNs), with the goal of conducting long term
prediction of the dynamics of the unknown system. In many real-world
applications, data from time-dependent systems are often collected on a time
scale that is coarser than desired, due to various restrictions during the data
acquisition process. Consequently, the observed dynamics can be severely
under-sampled and do not reflect the true dynamics of the underlying …

arxiv dynamics learning scale