RGFlow uses flow-based neural networks to learn bijective real-space RG transformations for the 2D phi^4 theory, identifying a Wilson-Fisher-like critical point and estimating the correlation length exponent.
Sugihara, Density matrix renormalization group in a two-dimensionalλϕ 4 hamiltonian lattice model, Journal of High Energy Physics2004, 007 (2004)
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Application of deep neural networks for computing the renormalization group flow of the two-dimensional phi^4 field theory
RGFlow uses flow-based neural networks to learn bijective real-space RG transformations for the 2D phi^4 theory, identifying a Wilson-Fisher-like critical point and estimating the correlation length exponent.