  Currently, over half of the computing power at CERN GRID is used to run High
Energy Physics simulations. The recent updates at the Large Hadron Collider
(LHC) create the need for developing more efficient simulation methods. In
particular, there exists a demand for a fast simulation of the neutron Zero
Degree Calorimeter, where existing Monte Carlo-based methods impose a
significant computational burden. We propose an alternative approach to the
problem that leverages machine learning. Our solution utilises neural network
classifiers and generative models to directly simulate the response of the
calorimeter. In particular, we examine the performance of variational
autoencoders and generative adversarial networks, expanding the GAN
architecture by an additional regularisation network and a simple, yet
effective postprocessing step. Our approach increases the simulation speed by 2
orders of magnitude while maintaining the high fidelity of the simulation.
