Learning spatio-temporal patterns with Neural Cellular Automata
Fig 4
A: loss as a function of time sampling t. Training loss shows the minimum loss during training epochs, averaged over 4 random initialisations, with standard deviation as error bars. Test loss shows how the best trained NCA (minimal training loss) performs on unseen initial conditions. B: Initial condition and true state (PDE simulation) at n = 2048. C: Snapshots of NCA trajectories (at n = 2048) based on unseen initial conditions, with varying time sampling t. Each NCA is trained for 4000 epochs, with a mini-batch size B = 64.