  We present the 3DGAN for the simulation of a future high granularity
calorimeter output as three-dimensional images. We prove the efficacy of
Generative Adversarial Networks (GANs) for generating scientific data while
retaining a high level of accuracy for diverse metrics across a large range of
input variables. We demonstrate a successful application of the transfer
learning concept: we train the network to simulate showers for electrons from a
reduced range of primary energies, we then train further for a five times
larger range (the model could not train for the larger range directly). The
same concept is extended to generate showers for other particles (photons and
neutral pions) depositing most of their energies in electromagnetic
interactions. In addition, the generation of charged pion showers is also
explored, a more accurate effort would require additional data from other
detectors not included in the scope of the current work. Our further
contribution is a demonstration of using GAN-generated data for a practical
application. We train a third-party network using GAN-generated data and prove
that the response is similar to a network trained with data from the Monte
Carlo simulation. The showers generated by GAN present accuracy within $10\%$
of Monte Carlo for a diverse range of physics features, with three orders of
magnitude speedup. The speedup for both the training and inference can be
further enhanced by distributed training.
