  We present an approach to identify concise equations from data using a
shallow neural network approach. In contrast to ordinary black-box regression,
this approach allows understanding functional relations and generalizing them
from observed data to unseen parts of the parameter space. We show how to
extend the class of learnable equations for a recently proposed equation
learning network to include divisions, and we improve the learning and model
selection strategy to be useful for challenging real-world data. For systems
governed by analytical expressions, our method can in many cases identify the
true underlying equation and extrapolate to unseen domains. We demonstrate its
effectiveness by experiments on a cart-pendulum system, where only 2 random
rollouts are required to learn the forward dynamics and successfully achieve
the swing-up task.
