A renormalization-group (RG) guided machine-learning algorithm for lattice field generation based on approximate inversion of an RG transformation based on approximate inversion of an RG transformation is introduced.
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 $\phi^4$ theory at criticality, a flow trained at $L\le32$ is reused recursively in cascades reaching $L=2048$ with correct long-distance physics.
An algorithm for generating lattice field configurations based on the approximate inversion of a renormalization-group blocking transformation using a ``perfect blocking''condition so that the blocked lattice distribution is well approximated by a simple coarse action.
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