13 citations · 53 across the 7 of their papers we have counts for
7 papers · 1 filter
MLMC: Machine Learning Monte Carlo for Lattice Gauge Theory
Sam Foreman, Xiao-Yong Jin, James C. Osborn
We present a trainable framework for efficiently generating gauge configurations, and discuss ongoing work in this direction. In particular, we consider the problem of sampling con…
Lattice QCD and Particle Physics
Andreas S. Kronfeld, Tanmoy Bhattacharya, Thomas Blum +79
Contribution from the USQCD Collaboration to the Proceedings of the US Community Study on the Future of Particle Physics (Snowmass 2021).
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…
RG inspired Machine Learning for lattice field theory
S. Foreman, J. Giedt, Y. Meurice +1
Machine learning has been a fast growing field of research in several areas dealing with large datasets. We report recent attempts to use Renormalization Group (RG) ideas in the co…