2022/01/05 by Xiao-Yong Jin, Jin, Xiao-Yong · 1 citation
Computer Science · #Computational Physics and Python Applications #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat)
paper · pdf · doi:10.48550/arxiv.2201.01862
openalex publication_date 2022/01/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a generic construction of Lie group agnostic and gauge covariant neural networks, and introduce constraints to make the neural networks continuous differentiable and invertible. We combine such neural networks and build gauge field transformations that is suitable for Hybrid Monte Carlo (HMC). We use HMC to sample lattice gauge configurations in the transformed space by the neural network parameterized gauge field transformations. Tested with 2D U(1) pure gauge systems at a range of couplings and lattice sizes, compared with direct HMC sampling, the neural network transformed HMC (NTHMC) generates Markov chains of gauge configurations with improved tunneling of topological charges, while allowing less force calculations as the lattice coupling increases.