Inverse renormalization group of spin glasses
This enables more efficient exploration of disordered systems like spin glasses without relying on supercomputers, though it appears incremental in method.
The authors tackled the problem of generating large-scale spin glass configurations by proposing inverse renormalization group transformations, using machine learning to construct rescaled lattices up to V'=128^3 from V=8^3 and extracting two critical exponents.
We propose inverse renormalization group transformations to construct approximate configurations for lattice volumes that have not yet been accessed by supercomputers or large-scale simulations in the study of spin glasses. Specifically, starting from lattices of volume $V=8^{3}$ in the case of the three-dimensional Edwards-Anderson model we employ machine learning algorithms to construct rescaled lattices up to $V'=128^{3}$, which we utilize to extract two critical exponents. We conclude by discussing how to incorporate numerical exactness within inverse renormalization group methods of disordered systems, thus opening up the opportunity to explore a sustainable and energy-efficient generation of exact configurations for increasing lattice volumes without the use of dedicated supercomputers.