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Learning spatio-temporal patterns with Neural Cellular Automata

Fig 3

Snapshots taken from the training data used for learning PDE dynamics.

PDE is run for N = 1024 steps with timestep 1 and D_A = 0.1, D_B = 0.05, α = 0.06230, γ = 0.06268.

doi: https://doi.org/10.1371/journal.pcbi.1011589.g003