2021/05/07 by Sam Foreman, Foreman, Sam, Xiao-Yong Jin +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Model Reduction and Neural Networks #Quantum many-body systems #Statistical Mechanics (cond-mat.stat-mech) #cond-mat.stat-mech #cs.LG #hep-lat #stat.ML
paper · pdf · doi:10.48550/arxiv.2105.03418
8 pages, 7 figures, Published as a workshop paper at ICLR 2021 SimDL Workshop
arxiv created 2021/05/07 · openalex publication_date 2021/05/07 · arxiv updated 2021/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 .