  Heterogeneous materials such as biological tissue scatter light in random,
yet deterministic, ways. Wavefront shaping can reverse the effects of
scattering to enable deep-tissue microscopy. Such methods require either
invasive access to the internal field or the ability to numerically compute it.
However, calculating the coherent field on a scale relevant to microscopy
remains excessively demanding for consumer hardware. Here we show how a
recurrent neural network can mirror Maxwell's equations without training. By
harnessing public machine learning infrastructure, such \emph{Scattering
Network} can compute the $633\;\textrm{nm}$-wavelength light field throughout a
$25\;\textrm{mm}^2$ or $176^3\;\mu\textrm{m}^3$ scattering volume. The
elimination of the training phase cuts the calculation time to a minimum and,
importantly, it ensures a fully deterministic solution, free of any training
bias. The integration with an open-source electromagnetic solver enables any
researcher with an internet connection to calculate complex light-scattering in
volumes that are larger by two orders of magnitude
