  In 1950 the first successful numerical weather forecast was obtained by
solving the barotropic vorticity equation using the Electronic Numerical
Integrator and Computer (ENIAC), which marked the beginning of the age of
numerical weather prediction. Here, we ask the question of how these numerical
forecasts would have turned out, if machine learning based solvers had been
used instead of standard numerical discretizations. Specifically, we recreate
these numerical forecasts using physics-informed neural networks. We show that
physics-informed neural networks provide an easier and more accurate
methodology for solving meteorological equations on the sphere, as compared to
the ENIAC solver.
