5 papers · 1 filter
Learning Low Modes of the Wilson Dirac Operator with Gauge-Equivariant Networks
Minjae Choi, Hiroshi Ohno
In adaptive multigrid, test vectors that better approximate the low-mode subspace of the Wilson Dirac operator can reduce the solve time, but their construction can be costly.…
Bias-Corrected Machine-Learning Estimation of Chiral Condensate Cumulants: A Retrospective Lattice QCD Case Study
Benjamin J. Choi, Hiroshi Ohno, Akio Tomiya
We present a retrospective case study of bias-corrected machine learning (ML) estimates of traces of the inverse Dirac operator, (), using a fixed la…
A Machine Learning Approach for Lattice Gauge Fixing
Ho Hsiao, Benjamin J. Choi, Hiroshi Ohno +1
Gauge fixing is an essential step in lattice QCD calculations, particularly for studying gauge-dependent observables. Traditional iterative algorithms are computationally expensive…
Machine Learning-Based Estimation of Cumulants of Chiral Condensate via Multi-Ensemble Reweighting with Deborah.jl
Benjamin J. Choi, Hiroshi Ohno, Akio Tomiya
We investigate a bias-corrected machine learning (ML) strategy for estimating traces of the inverse Dirac operator, (), motivated by the need for hi…
Lattice Gauge Theory via LLVM-Level Automatic Differentiation
Yuki Nagai, Akio Tomiya, Hiroshi Ohno
We enable the automatic construction of Hybrid Monte Carlo (HMC) forces in lattice gauge theory by performing reverse-mode automatic differentiation at the level of optimized LLVM…