13 citations · 14 across the 2 of their papers we have counts for
4 papers
Applications of Machine Learning to Lattice Quantum Field Theory
Denis Boyda, Salvatore Calì, Sam Foreman +8
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In th…
LeapfrogLayers: A Trainable Framework for Effective Topological Sampling
Sam Foreman, Xiao-Yong Jin, James C. Osborn
We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D lattice gauge theory. We show an improv…
Deep Learning Hamiltonian Monte Carlo
Sam Foreman, Xiao-Yong Jin, James C. Osborn
We generalize the Hamiltonian Monte Carlo algorithm with a stack of neural network layers and evaluate its ability to sample from different topologies in a two dimensional lattice…
Examples of renormalization group transformations for image sets
Samuel Foreman, Joel Giedt, Yannick Meurice +1
Using the example of configurations generated with the worm algorithm for the two-dimensional Ising model, we propose renormalization group (RG) transformations, inspired by the te…