  We present an implementation of an explainable and physics-aware machine
learning model capable of inferring the underlying physics of high-energy
particle collisions using the information encoded in the energy-momentum
four-vectors of the final state particles. We demonstrate the proof-of-concept
of our White Box AI approach using a Generative Adversarial Network (GAN) which
learns from a DGLAP-based parton shower Monte Carlo event generator. We show,
for the first time, that our approach leads to a network that is able to learn
not only the final distribution of particles, but also the underlying parton
branching mechanism, i.e. the Altarelli-Parisi splitting function, the ordering
variable of the shower, and the scaling behavior. While the current work is
focused on perturbative physics of the parton shower, we foresee a broad range
of applications of our framework to areas that are currently difficult to
address from first principles in QCD. Examples include nonperturbative and
collective effects, factorization breaking and the modification of the parton
shower in heavy-ion, and electron-nucleus collisions.
