Renormalization-guided cascade upscaling for lattice field generation
Anna Hasenfratz, Ethan T. Neil, Letizia Parato, Noah Schwartz
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 L32 is reused recursively in cascades reaching L=2048 with correct long-distance physics.
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