Chris has posted the relevant papers in a sibling comment, but let me try to give an intuitive explanation. Say you have some sort of electronic device like an amplifier. It is made out of many individual transistors and parasitics, but its time evolution is overall guided mostly by the amplifier characteristics its supposed to implement plus boundary effects and corrections from the parasitics. So what you try to do is to learn a simpler system of equations that captures the time evolution plus a (highly-nonlinear, complicated) projection that recovers the signals of interest (if you don't care about reproducing interior signals, you can usually get away with a simpler model). Of course it's not obvious that this should actually work (though of course picking a model of the same size as the system plus an identity projection is always possible, so it'll always work for some size of the model being learned, just at that point you don't get a speedup), but our initial results show that it does seem to be very promising.