  We present 2-dimensional turbulent electric field calculations via
physics-informed deep learning consistent with (i) drift-reduced Braginskii
theory under the framework of an axisymmetric fusion plasma with purely
toroidal field and (ii) experimental estimates of the fluctuating electron
density and temperature on open field lines obtained from analysis of gas puff
imaging of a discharge on the Alcator C-Mod tokamak. The inclusion of effects
from the locally puffed atomic helium on particle and energy sources within the
reduced plasma turbulence model are found to strengthen correlations between
the electric field and electron pressure. The neutrals are also directly
associated with broadening the distribution of turbulent field amplitudes and
increasing ${\bf E \times B}$ shearing rates. This demonstrates a novel
approach in plasma experiments by solving for nonlinear dynamics consistent
with partial differential equations and data without encoding explicit boundary
nor initial conditions.
