  Identifying the governing equations of a nonlinear dynamical system is key to
both understanding the physical features of the system and constructing an
accurate model of the dynamics that generalizes well beyond the available data.
We propose a machine learning framework for discovering these governing
equations using only partial observations, combining an encoder for state
reconstruction with a sparse symbolic model. Our tests show that this method
can successfully reconstruct the full system state and identify the underlying
dynamics for a variety of ODE and PDE systems.
