  Understanding the physics of large cosmological surveys down to small
(nonlinear) scales will significantly improve our knowledge of the Universe.
Large N-body simulations have been built to obtain predictions in the
non-linear regime. However, N-body simulations are computationally expensive
and generate large amount of data, putting burdens on storage. These data are
snapshots of the simulated Universe at different times, and fine sampling is
necessary to accurately save its whole history. We employ a deep neural network
model to predict the nonlinear N-body simulation at an intermediate time step
given two widely separated snapshots. Our results outperform the cubic Hermite
interpolation benchmark method in interpolating N-body simulations. This work
can greatly reduce the storage requirement and allow us to reconstruct the
cosmic history from far fewer snapshots of the universe.
