  The solution of problems in physics is often facilitated by a change of
variables. In this work we present neural transformations to learn symmetries
of Hamiltonian mechanical systems. Maintaining the Hamiltonian structure
requires novel network architectures that parametrize symplectic
transformations. We demonstrate the utility of these architectures by learning
the structure of integrable models. Our work exemplifies the adaptation of
neural transformations to a family constrained by more than the condition of
invertibility, which we expect to be a common feature of applications of these
methods.
