  In todays age of data, discovering relationships between different variables
is an interesting and a challenging problem. This problem becomes even more
critical with regards to complex dynamical systems like weather forecasting and
econometric models, which can show highly non-linear behavior. A method based
on mutual information and deep neural networks is proposed as a versatile
framework for discovering non-linear relationships ranging from functional
dependencies to causality. We demonstrate the application of this method to
actual multivariable non-linear dynamical systems. We also show that this
method can find relationships even for datasets with small number of
datapoints, as is often the case with empirical data.
