Deep Learning Hamiltonian Monte Carlo

• URL: http://arxiv.org/abs/2105.03418v1
• Date: Fri, 7 May 2021 17:50:18 GMT
• Title: Deep Learning Hamiltonian Monte Carlo
• Authors: Sam Foreman, Xiao-Yong Jin, and James C. Osborn
• Abstract summary: We generalize the Hamiltonian Monte Carlo algorithm with a stack of neural network layers. We demonstrate that our model is able to successfully mix between modes of different topologies.
• Score: 0.6554326244334867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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