# Inverse renormalization group of spin glasses

arXiv: https://arxiv.org/abs/2310.12631 · HTML: https://mlsanity.com/paper/2310.12631/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [A guide to convolution arithmetic for deep learning](https://mlsanity.com/paper/1603.07285/) (2016)
- [Neural Network Renormalization Group](https://mlsanity.com/paper/1802.02840/) (2018)

## Built on by (most cited first, 1 of 1)

- [Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics](https://mlsanity.com/paper/2501.05580/) (2025)

## Abstract (arXiv)

We propose inverse renormalization group transformations to construct approximate configurations for lattice volumes that have not yet been accessed by supercomputers or large-scale simulations in the study of spin glasses. Specifically, starting from lattices of volume V=83 in the case of the three-dimensional Edwards-Anderson model we employ machine learning algorithms to construct rescaled lattices up to V'=1283, which we utilize to extract two critical exponents. We conclude by discussing how to incorporate numerical exactness within inverse renormalization group methods of disordered systems, thus opening up the opportunity to explore a sustainable and energy-efficient generation of exact configurations for increasing lattice volumes without the use of dedicated supercomputers.