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.
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The second plateau at about 3/5 the first in 3D QHE arises from spin-density-wave order induced by nesting between spin-up and spin-down Landau bands after a Lifshitz transition.
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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.
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3D Quantum Hall Effect with Two Distinct Plateaus
The second plateau at about 3/5 the first in 3D QHE arises from spin-density-wave order induced by nesting between spin-up and spin-down Landau bands after a Lifshitz transition.