Learning spatio-temporal patterns with Neural Cellular Automata
Fig 6
A: loss as a function of noise intensity ξ, on unseen initial condition. ξ interpolates between the PDE trajectory (ξ = 0) and uniform noise (ξ = 1) as the training data for the NCA. Also shown is an interpolating moving average ± standard deviation, as there is significant variation introduced by random parameter initialisation. 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 noise intensity ξ. Each NCA is trained for 4000 epochs, with a mini-batch size B = 64.