Construction of cellular automata using machine learning models

The paper is devoted to the development and study of cellular automata approximation methods using machine learning models. Cellular automata are models used to study the dynamics of complex systems based on simple interaction rules. In recent years, machine learning models have become powerful tools in the field of data processing. The paper examines approaches to predicting cellular automata rules using machine learning models, considers their advantages and limitations, and proposes metrics for assessing the quality of cellular automata state predictions and the dependence of cellular automata state prediction on the number of cellular automata rule models entering the input for training. The study aims to understand how machine learning models can be used to analyze and model complex systems based on cellular automata, as well as possible prospects for the development of this approach. Based on the proposed metrics, a comparative analysis of the effectiveness of various machine learning models in predicting cellular automata rules is carried out.