  A key uncertainty in the design and development of magnetic confinement
fusion energy reactors is predicting edge plasma turbulence. An essential step
in overcoming this uncertainty is the validation in accuracy of reduced
turbulent transport models. Drift-reduced Braginskii two-fluid theory is one
such set of reduced equations that has for decades simulated boundary plasmas
in experiment, but significant questions exist regarding its predictive
ability. To this end, using a novel physics-informed deep learning framework,
we demonstrate the first ever direct quantitative comparisons of turbulent
field fluctuations between electrostatic two-fluid theory and electromagnetic
gyrokinetic modelling with good overall agreement found in magnetized helical
plasmas at low normalized pressure. This framework is readily adaptable to
experimental and astrophysical environments, and presents a new technique for
the numerical validation and discovery of reduced global plasma turbulence
models.
