  We propose a new method to efficiently compute load-flows (the steady-state
of the power-grid for given productions, consumptions and grid topology),
substituting conventional simulators based on differential equation solvers. We
use a deep feed-forward neural network trained with load-flows precomputed by
simulation. Our architecture permits to train a network on so-called "n-1"
problems, in which load flows are evaluated for every possible line
disconnection, then generalize to "n-2" problems without retraining (a clear
advantage because of the combinatorial nature of the problem). To that end, we
developed a technique bearing similarity with "dropout", which we named "guided
dropout".
