  The nonlinear dimer obtained through the nonlinear Schr{\"o}dinger equation
has been a workhorse for the discovery the role nonlinearity plays in strongly
interacting systems. While the analysis of the stationary states demonstrates
the onset of a symmetry broken state for some degree of nonlinearity, the full
dynamics maps the system into an effective $\phi^4$ model. In this latter
context, the self-trapping transition is an initial condition dependent
transfer of a classical particle over a barrier set by the nonlinear term. This
transition has been investigated analytically and mathematically it is
expressed through the hyperbolic limit of Jacobian elliptic functions. The aim
of the present work is to recapture this transition through the use of methods
of Artificial Intelligence (AI). Specifically, we used a physics motivated
machine learning model that is shown to be able to capture the original dynamic
self-trapping transition and its dependence on initial conditions. Exploitation
of this result in the case of the non-degenerate nonlinear dimer gives
additional information on the more general dynamics and helps delineate linear
from nonlinear localization. This work shows how AI methods may be embedded in
physics and provide useful tools for discovery.
