Search Results for author:
Found 4 papers, 3 papers with code
Applications of Machine Learning to Lattice Quantum Field Theory
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.
LeapfrogLayers: A Trainable Framework for Effective Topological Sampling
We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D $U(1)$ lattice gauge theory.
HMC with Normalizing Flows
We propose using Normalizing Flows as a trainable kernel within the molecular dynamics update of Hamiltonian Monte Carlo (HMC).
Deep Learning Hamiltonian Monte Carlo
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 gauge theory.
