PreprintarXiv licencearXiv
Renormalization-guided cascade upscaling for lattice field generation
Abstract
We introduce a renormalization-group (RG) guided machine-learning algorithm for lattice field generation based on approximate inversion of an RG transformation. A ``perfect blocking'' construction supplies equilibrated long-distance modes, while a conditional normalizing flow reconstructs short-distance details and brief rethermalization removes residual errors. In 2D ϕ4 theory at criticality, a flow trained at L≤32 is reused recursively in cascades reaching L=2048 with correct long-distance physics.
Related
- Renormalization-guided inverse blocking for lattice field generation: construction and validation
- RI/SMOM renormalization for lattice QCD: bilinear and three-quark operators
- One-loop mixing matrix of the N=1 SYM Supercurrent operator upon renormalization on the Lattice using stout-smeared Links
- Flowed quark field renormalization in lattice QCD: A Ward-identity approach and its validation using quark bilinears
- High‐Pressure Modulation of Breathing Kagome Lattice: Cascade of Lifshitz Transitions and Evolution of the Electronic Structure
BibTeX
@misc{hasenfratz2026renormalizationguided,
title = {{Renormalization-guided cascade upscaling for lattice field generation}},
author = {Anna Hasenfratz and Ethan T. Neil and Letizia Parato and Noah Schwartz},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.28581},
eprint = {2608.28581},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.28581},
}
RIS
TY - UNPB
TI - Renormalization-guided cascade upscaling for lattice field generation
AU - Anna Hasenfratz
AU - Ethan T. Neil
AU - Letizia Parato
AU - Noah Schwartz
PY - 2026
DA - 2026/10/13
PB - arXiv
DO - 10.48550/arxiv.2608.28581
UR - https://arxiv.org/abs/2608.28581
AB - We introduce a renormalization-group (RG) guided machine-learning algorithm for lattice field generation based on approximate inversion of an RG transformation. A ``perfect blocking'' construction supplies equilibrated long-distance modes, while a conditional normalizing flow reconstructs short-distance details and brief rethermalization removes residual errors. In 2D ϕ^4 theory at criticality, a flow trained at L≤32 is reused recursively in cascades reaching L=2048 with correct long-distance physics.
ER -