Statistical Physics through the Lens of Real-Space Mutual Information

Doruk Efe Gökmen, Zohar Ringel, Sebastian D. Huber, Maciej Koch-Janusz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Identifying the relevant degrees of freedom in a complex physical system is a key stage in developing effective theories in and out of equilibrium. The celebrated renormalization group provides a framework for this, but its practical execution in unfamiliar systems is fraught with ad hoc choices, whereas machine learning approaches, though promising, lack formal interpretability. Here we present an algorithm employing state-of-the-art results in machine-learning-based estimation of information-theoretic quantities, overcoming these challenges, and use this advance to develop a new paradigm in identifying the most relevant operators describing properties of the system. We demonstrate this on an interacting model, where the emergent degrees of freedom are qualitatively different from the microscopic constituents. Our results push the boundary of formally interpretable applications of machine learning, conceptually paving the way toward automated theory building.

Original languageAmerican English
Article number240603
JournalPhysical Review Letters
Volume127
Issue number24
DOIs
StatePublished - 10 Dec 2021

Bibliographical note

Publisher Copyright:
© 2021 American Physical Society.

Fingerprint

Dive into the research topics of 'Statistical Physics through the Lens of Real-Space Mutual Information'. Together they form a unique fingerprint.

Cite this