  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
extrapolate the exponents of the 2D Ising critical point towards the
thermodynamic limit, which results in good agreement with theory.
