Yeah, they're predicting the outcome of a chaotic but deterministic process based on the output data given.

Surely they're discovering hidden (or obvious) regularities based specifically on the process being deterministic.

As a counter example, the Smale Horseshoe [1] is an example of a inherently unpredictable map - it's approximately equivalent to choosing a real number "at random" and attempting to "predict" each digit as you find it. Sure, if some implementation has chosen a number with repeating digits, you can "predict" those digits but this would have nothing to do with being an oracle for algorithm itself.

More broadly, I think it's pretty established that any realistic model of weather is kind of an extension of this concept - it depends unstably on it's initial conditions (small changes in initial conditions result in large changes in final result after some period).

[1] https://en.wikipedia.org/wiki/Horseshoe_map

This interpretation sounds right to me. I found it cool that the article characterizes success in terms of prediction accuracy out to N "Lyapunov times", a concept that is supposed to incorporate the unpredictability of the system. So perhaps discovering these regularities proves that in this implementation, the true Lyapunov time is longer.

I believe (but I might be wrong) that are compared the results of two different methods: (a) "algorithmic" where solution(t+1) = algo(solution(t)) and errors accumulate with time steps, vs. (b) based on sufficiently many available pairs (initial_1, solution_1), ...,(initial_N,solution_N), each solution_i a function of time t \in [0,T], a NN (or another gadget) builds a function ALGO(initial)=solution so that the errors in time of solution(t) wrt real_solution(t) are uniform, where real_solution is the real solution for the initial condition initial.