Machine learning in physics: a short guide
It is an incremental review for researchers in physics, summarizing existing methods without new results.
This review provides an overview of machine learning concepts and applications in physics, covering supervised, unsupervised, and reinforcement learning, along with specialized topics like causal inference and deep learning, while discussing challenges and perspectives.
Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised, unsupervised, and reinforcement learning, as well as more specialized topics such as causal inference, symbolic regression, and deep learning. We present some of the principal applications of machine learning in physics and discuss the associated challenges and perspectives.