arxiv:1807.10250

Examples of renormalization group transformations for image sets

Published on Jul 26, 2018

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Abstract

Rigorous renormalization group transformations are proposed for image sets, inspired by tensor RG, to explore criticality through principal component analysis and link occupation changes.

AI-generated summary

Using the example of configurations generated with the worm algorithm for the two-dimensional Ising model, we propose renormalization group (RG) transformations, inspired by the tensor RG, that can be applied to sets of images. We relate criticality to the logarithmic divergence of the largest principal component. We discuss the changes in link occupation under the RG transformation, suggest ways to obtain data collapse, and compare with the two state tensor RG approximation near the fixed point.

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