Análisis de sistemas complejos usando machine learning

Abstract

Nowadays, we can find complex systems in a great variety of fields, such as biology, physics, engineering or social systems. From fluid dynamics to biomolecular interactions, we can find complex systems everywhere, and it is not an easy task to predict their behaviour. Cellular automata are considered great techniques of modelling complex systems. In cellular automata, some simple rules lead sometimes to emergent chaotic or complex behaviours that are interesting in the context of some real-life complex models. Machine Learning algorithms are well known for identifying underlaying patterns in complex structures. In this paper, cellular automata and some Machine Learning algorithms, such as Neural Networks or k-Nearest Neighbours are introduced, and after that some of them are used to analyze cellular automata. The aim of this paper is to define a way of working with cellular automata and machine learning that can be expanded later with new algorithms, new automata or more data.