Dreaming up scale invariance via inverse renormalization group

ArXi:2506.04016v2 Announce Type: replace-cross We explore how minimal neural networks can invert the renormalization group (RG) coarse-graining procedure in the two-dimensional Ising model, effectively ``dreaming up'' microscopic configurations from coarse-grained states. This task - formally impossible at the level of configurations - can be approached probabilistically, allowing machine learning models to reconstruct scale-invariant distributions without relying on microscopic input.