  We present a data-driven modeling strategy to overcome improperly modeled
dynamics for systems exhibiting complex spatio-temporal behaviors. We propose a
Deep Learning framework to resolve the differences between the true dynamics of
the system and the dynamics given by a model of the system that is either
inaccurately or inadequately described. Our machine learning strategy leverages
data generated from the improper system model and observational data from the
actual system to create a neural network to model the dynamics of the actual
system. We evaluate the proposed framework using numerical solutions obtained
from three increasingly complex dynamical systems. Our results show that our
system is capable of learning a data-driven model that provides accurate
estimates of the system states both in previously unobserved regions as well as
for future states. Our results show the power of state-of-the-art machine
learning frameworks in estimating an accurate prior of the system's true
dynamics that can be used for prediction up to a finite horizon.
