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LeapfrogLayers: A Trainable Framework for Effective Topological Sampling. (arXiv:2112.01582v2 [hep-lat] UPDATED)
Jan. 17, 2022, 2:11 a.m. | Sam Foreman, Xiao-Yong Jin, James C. Osborn
cs.LG updates on arXiv.org arxiv.org
We introduce LeapfrogLayers, an invertible neural network architecture that
can be trained to efficiently sample the topology of a 2D $U(1)$ lattice gauge
theory. We show an improvement in the integrated autocorrelation time of the
topological charge when compared with traditional HMC, and look at how
different quantities transform under our model. Our implementation is open
source, and is publicly available on github at
https://github.com/saforem2/l2hmc-qcd.
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