  The application of machine learning methods to particle physics often doesn't
provide enough understanding of the underlying physics. An interpretable model
which provides a way to improve our knowledge of the mechanism governing a
physical system directly from the data can be very useful. In this paper, we
introduce a simple artificial physical generator based on the Quantum
chromodynamical (QCD) fragmentation process. The data simulated from the
generator are then passed to a neural network model which we base only on the
partial knowledge of the generator. We aim to see if the interpretation of the
generated data can provide the probability distributions of basic processes of
such a physical system. This way, some of the information we omitted from the
network model on purpose is recovered. We believe this approach can be
beneficial in the analysis of real QCD processes.
