Machine Learning in the Physical Sciences

If, like me, you feel a bit left behind by goings-on in the field of Machine Learning and how it impacts on physics then there’s now a very comprehensive review by Carleo et al on the arXiv.

Here is a picture from the paper, which I have included so that this post has a picture in it:

The abstract reads:

Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years. We review in a selective way the recent research on the interface between machine learning and physical sciences.This includes conceptual developments in machine learning (ML) motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross-fertilization between the two fields. After giving basic notion of machine learning methods and principles, we describe examples of how statistical physics is used to understand methods in ML. We then move to describe applications of ML methods in particle physics and cosmology, quantum many body physics, quantum computing, and chemical and material physics. We also highlight research and development into novel computing architectures aimed at accelerating ML. In each of the sections we describe recent successes as well as domain-specific methodology and challenges.

The next step after Machine Learning will of course be Machine Teaching…


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