2021/05/07 by Sam Foreman, Foreman, Sam, Xiao-Yong Jin +3 · 12 citations
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Combinatorics #Computer science #FOS: Computer and information sciences #FOS: Physical sciences #Gauge theory #Hamiltonian (control theory) #Hamiltonian lattice gauge theory #High Energy Physics - Lattice (hep-lat) #Hybrid Monte Carlo #Ising model #Lattice (music) #Lattice gauge theory #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Markov chain Monte Carlo #Mathematical optimization #Mathematics #Model Reduction and Neural Networks #Monte Carlo method #Monte Carlo molecular modeling #Network topology #Particle physics #Physics #Quantum Monte Carlo #Quantum chromodynamics #Quantum many-body systems #Statistical Mechanics (cond-mat.stat-mech) #Statistical physics #Topology (electrical circuits) #cond-mat.stat-mech #cs.LG #hep-lat #stat.ML
paper · pdf · doi:10.48550/arxiv.2105.03418
published in arXiv (Cornell University) (Cornell University) · 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 .