Given that ANNs are universal function approximators, it is natural that one would use them to actually generate a simplified model of simulations. What you see here, taking an already accurate model and generating an approximate implementation, is definitely rare but not unexpected. Long-running simulations can have their run-time drastically compressed simply by developing a neural network capable of being parallelized on GPUs, as in this paper. The ANN run time could be further compressed by generating a single-hidden-layer equivalent. Even though they are known to be exponentially larger than multiple hidden layers, the implementation can be valuable in real-time systems where analysis latency is at a premium, e.g. image recognition in machine control systems.