Machine learning and the renormalisation group

Maciej Koch-Janusz
Institute for Theoretical Physics, ETH Zurich, Switzerland

Physical systems differing in their microscopic details often display strikingly similar behaviour when probed at macroscopic scales. Those universal properties, largely determining their physical characteristics, are revealed by the renormalization group (RG) procedure, which systematically retains ‘slow’ degrees of freedom and integrates out the rest. We demonstrate a machine-learning algorithm based on a model-independent, information-theoretic characterization of a real-space RG  capable of identifying the relevant degrees of freedom and executing RG steps iteratively without any prior knowledge about the system.  We apply it to classical statistical physics problems in 1 and 2D: we demonstrate RG flow and extract critical exponents. As a matter of introduction, I will briefly review recent progress in applying ML techniques in condensed matter physics.