Lecturer: Maciek Koch Janusz
Abstract:
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. We also prove
results about optimality of the procedure.