Sparse Statistical Modeling in Condensed Matter Physics

TL;DR

This paper investigates the use of sparse statistical modeling in condensed matter physics, demonstrating its advantages in interpretability and effectiveness on small datasets compared to traditional AI methods.

Contribution

It introduces sparse statistical modeling as a novel approach for condensed matter physics problems, highlighting its interpretability and suitability for small data sets.

Findings

Sparse modeling outperforms traditional AI methods in certain physics problems.

It effectively handles small datasets, providing physical insights.

Offers improved interpretability over deep learning methods.

Abstract

In this work we explore the possibility of using sparse statistical modeling in condensed matter physics. The procedure is employed to two well known problems: elemental superconductors and heavy fermions, and was shown that in most cases performs better than other AI methods, such as machine or deep learning. More importantly, sparse modeling has two major advantages over other methods: the ability to deal with small data sets and in particular its interpretabilty. Namely, sparse modeling can provide insight into the calculation process and allow the users to give physical interpretation of their results. We argue that many other problems in condensed matter physics would benefit from these properties of sparse statistical modeling.