  Developing high-precision models of the nuclear force and propagating the
associated uncertainties in quantum many-body calculations of nuclei and
nuclear matter remain key challenges for ab initio nuclear theory. In the
present work we demonstrate that generative machine learning models can
construct novel instances of the nucleon-nucleon interaction when trained on
existing potentials from the literature. In particular, we train the generative
model on nucleon-nucleon potentials derived at second and third order in chiral
effective field theory and at three different choices of the resolution scale.
We then show that the model can be used to generate samples of the
nucleon-nucleon potential drawn from a continuous distribution in the
resolution scale parameter space. The generated potentials are shown to produce
high-quality nucleon-nucleon scattering phase shifts. This work provides an
important step toward a comprehensive estimation of theoretical uncertainties
in nuclear many-body calculations that arise from the arbitrary choice of
nuclear interaction and resolution scale.
