Identifying hidden parameters in cellular automata with neural networks

Abstract

Cellular Automata models are commonly utilized in studying the dynamics of complex systems in physics, biology, and social sciences. However, the behavior of CAs is often influenced by hidden parameters, which are difficult to determine. Since a CA assigns discrete values to each cell in a domain, it is tempting to use neural networks to classify these hidden parameters as neural networks have been used extensively to classify images. In this thesis, we propose an approach to identify the hidden jump parameters, which affect the behavior of CA models, using neural networks.

This study’s findings indicate that neural networks are a powerful tool to identify hidden parameters in CA models. They have the potential to greatly increase the performance of parameter identification since the identification process is usually much faster than that of comparable statistical methods. We believe that our approach can be used in physics, biology, and social sciences, Complex systems are modeled using CA models.