Journal
PHYSICAL REVIEW B
Volume 99, Issue 7, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.99.075113
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Funding
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Canada Research Chair program
- Perimeter Institute for Theoretical Physics
- NVIDIA Corporation
- Government of Canada through Industry Canada
- Province of Ontario through the Ministry of Research Innovation
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Machine learning is becoming widely used in condensed matter physics. Inspired by the concept of image super-resolution, we propose a method to increase the size of lattice spin configurations using deep convolutional neural networks. Through supervised learning on Monte Carlo (MC) generated spin configurations, we train networks that invert real-space renormalization decimations. We demonstrate that super-resolution can reproduce thermodynamic observables that agree with MC calculations for the one- and two-dimensional Ising model at various temperatures. We find that it is possible to predict thermodynamic quantities for lattice sizes larger than those used in training by extrapolating the parameters of the network. We use this method to compute the critical exponents of the 2D Ising model, finding good agreement with theory.
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