I would like to point out a much more exciting modelling process, whereby neural networks extract the underlying boolean logic from simulation outputs: https://google-research.github.io/self-organising-systems/di...

I firmly believe that differentiable logic CA is the winner, in particular because it extracts the logic directly, and thus leads to generalize-able programs as opposed to staying stuck in matrix multiplication land.