PoS - Proceedings of Science
Volume 396 - The 38th International Symposium on Lattice Field Theory (LATTICE2021) - Oral presentation
LeapfrogLayers: A Trainable Framework for Effective Topological Sampling
S. Foreman*, X.y. Jin and J. Osborn
Full text: pdf
Pre-published on: May 16, 2022
Published on:
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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