  Purpose: A reliable model to simulate nuclear interactions is fundamental for
Ion-therapy. We already showed how BLOB ("Boltzmann-Langevin One Body"), a
model developed to simulate heavy ion interactions up to few hundreds of MeV/u,
could simulate also $^{12}$C reactions in the same energy domain. However, its
computation time is too long for any medical application. For this reason we
present the possibility of emulating it with a Deep Learning algorithm.
Methods: The BLOB final state is a Probability Density Function (PDF) of
finding a nucleon in a position of the phase space. We discretised this PDF and
trained a Variational Auto-Encoder (VAE) to reproduce such a discrete PDF. As a
proof of concept, we developed and trained a VAE to emulate BLOB in simulating
the interactions of $^{12}$C with $^{12}$C at 62 MeV/u. To have more control on
the generation, we forced the VAE latent space to be organised with respect to
the impact parameter ($b$) training a classifier of $b$ jointly with the VAE.
Results: The distributions obtained from the VAE are similar to the input ones
and the computation time needed to use the VAE as a generator is negligible.
Conclusions: We show that it is possible to use a Deep Learning approach to
emulate a model developed to simulate nuclear reactions in the energy range of
interest for Ion-therapy. We foresee the implementation of the generation part
in C++ and to interface it with the most used Monte Carlo toolkit: Geant4.
