A Review and Perspective on the Main Machine Learning Methods Applied to Physical Sciences

Abstract

Several types of numerical simulations have been used over the years in the Physical Sciences, to advance the real-life problems understanding. Among the statistical tools used for this are, for example: Monte Carlo simulations, such mechanisms have been used in various areas, however, today another tool is used, Machine Learning, which is a branch of Artificial Intelligence (AI). This article reviews sets of work that encompass various areas of the Physical Sciences, to mention some such as particle physics, quantum mechanics, condensed matter, among many others that have used some Machine Learning mechanisms to solve part of the problems raised in their research. In turn, a Machine Learning methods classification was carried out and it was identified which are the most used in Physical Sciences, something that is currently done in very few studies, as it requires extensive review work. The analysis carried out also allowed us to glimpse which areas of the Physical Sciences use Machine Learning the most and identify in which types of journals it is published more on the subject. The results obtained, show that there is currently a good number of works that interrelate Machine Learning and the Physical Sciences, and that this interrelation is increasing.