  We develop a machine learning algorithm to infer the emergent stochastic
equation governing the evolution of an order parameter of a many-body system.
We train our neural network to independently learn the directed force acting on
the order parameter as well as an effective diffusive noise. We illustrate our
approach using the classical Ising model endowed with Glauber dynamics, and the
contact process as test cases. For both models, which represent paradigmatic
equilibrium and nonequilibrium scenarios, the directed force and noise can be
efficiently inferred. The directed force term of the Ising model allows us to
reconstruct an effective potential for the order parameter which develops the
characteristic double-well shape below the critical temperature. Despite its
genuine nonequilibrium nature, such an effective potential can also be obtained
for the contact process and its shape signals a phase transition into an
absorbing state. Also, in contrast to the equilibrium Ising model, the presence
of an absorbing state renders the noise term dependent on the value of the
order parameter itself.
