  Chemical reactions in multidimensional systems are often described by a
rank-1 saddle, whose stable and unstable manifolds intersect in the normally
hyperbolic invariant manifold (NHIM). Trajectories started on the NHIM in
principle never leave this manifold when propagated forward or backward in
time. However, the numerical investigation of the dynamics on the NHIM is
difficult because of the instability of the motion. We apply a neural network
to describe time-dependent NHIMs and use this network to stabilize the motion
on the NHIM for a periodically driven model system with two degrees of freedom.
The method allows us to analyze the dynamics on the NHIM via Poincar\'e
surfaces of section (PSOS) and to determine the transition state (TS)
trajectory as a periodic orbit with the same periodicity as the driving saddle,
viz. a fixed point of the PSOS surrounded by near-integrable tori. Based on
Transition State Theory and a Floquet analysis of a periodic TS trajectory we
compute the rate constant of the reaction with significantly reduced numerical
effort compared to the propagation of a large trajectory ensemble.
