LeapfrogLayers: A Trainable Framework for Effective Topological Sampling
S. Foreman*,
X.y. Jin and
J.C. Osborn*: corresponding author
Pre-published on:
May 16, 2022
Published on:
July 08, 2022
Abstract
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://www.github.com/saforem2/l2hmc-qcd.
DOI: https://doi.org/10.22323/1.396.0508
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