Learning spatio-temporal patterns with Neural Cellular Automata
Fig 2
1D phase space representation of NCA trajectories, predictions and true states y(m, r).
Here M = 3, R = 2. The first batch (x(⋅,1)) is trained with re-initialised intermediate states, whereas the second batch (x(⋅,2)) is trained with propagated intermediate states.