Learning spatio-temporal patterns with Neural Cellular Automata

arXiv:2310.14809v2 Announce Type: replace-cross
Abstract: Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as …

abstract arxiv cellular combination cs.lg cs.ne dynamic dynamics identify images learn machine machine learning math.ds modelling nlin.ao nlin.ps patterns rules scale series temporal time series train type work