This study presents Dynamics Identification via Neural Cellular Automata (DINCA), an enhancement of Neural Cellular Automata (NCA) for modeling reaction-diffusion systems. The main advantage of DINCA is its ability to estimate the parameters of the reaction-diffusion equations that govern the examined system, using minimal data. We demonstrate the method’s application potential by showing its ability to model leopard pattern formation, by learning on only three images, while revealing the governing reaction-diffusion equations. This positions NCA-based methodologies as a viable tool for inferring partial differential equations. The code is available at https://github.com/koutefra/dinca .