Small neural networks invert the RG coarse-graining in the 2D Ising model to probabilistically generate critical configurations that reproduce scaling observables and nontrivial RG eigenvalues.
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Adding a continuous bond energy ε to 2D site percolation shifts the threshold smoothly and drives the correlation-length exponent ν from 1/2 through 4/3 to 1, as shown by Monte Carlo simulations and real-space RG that also reveal an energy-weighted correlation length and antiferromagnetic ordering,
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Dreaming up scale invariance via inverse renormalization group
Small neural networks invert the RG coarse-graining in the 2D Ising model to probabilistically generate critical configurations that reproduce scaling observables and nontrivial RG eigenvalues.
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Energy-Weighted Site Percolation in Two Dimensions
Adding a continuous bond energy ε to 2D site percolation shifts the threshold smoothly and drives the correlation-length exponent ν from 1/2 through 4/3 to 1, as shown by Monte Carlo simulations and real-space RG that also reveal an energy-weighted correlation length and antiferromagnetic ordering,