arxiv:2105.03418

Deep Learning Hamiltonian Monte Carlo

Published on May 7, 2021

Authors:

Sam Foreman ,

Abstract

The generalized Hamiltonian Monte Carlo algorithm using neural networks reduces computational cost in generating gauge field configurations across different topologies.

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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. We demonstrate that our model is able to successfully mix between modes of different topologies, significantly reducing the computational cost required to generated independent gauge field configurations. Our implementation is available at https://github.com/saforem2/l2hmc-qcd .

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