Doesn't that mean that the neural net learned itself a numerical method to compute the solution of the equation, and that it is close enough in terms of approximation up to 7 Lyapukov times, and after that time, the approximation becomes not good enough and the system can't predict?
I think it's blurring the line between modelling and simulation. You can find an efficient route down a hillside by pouring water down it, or a line of least resistance through a system by passing a current through it. Is the system learning a numerical solution, or performing one? I think this is like building a model of a system that is much closer to the territory than the map compared to a normal model, but still easier to work with than the territory.
It doesn't sound like too groundbreaking...