  Machine learning technology has the potential to dramatically optimise event
generation and simulations. We continue to investigate the use of neural
networks to approximate matrix elements for high-multiplicity scattering
processes. We focus on the case of loop-induced diphoton production through
gluon fusion and develop a realistic simulation method that can be applied to
hadron collider observables. Neural networks are trained using the one-loop
amplitudes implemented in the NJet C++ library and interfaced to the Sherpa
Monte Carlo event generator where we perform a detailed study for $2\to3$ and
$2\to4$ scattering problems. We also consider how the trained networks perform
when varying the kinematic cuts effecting the phase space and the reliability
of the neural network simulations.
