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Updated: Dec 6, 2025

The Diffusion of Passive Tracers in Laminar Shear Flow
Published on: May 1, 2018
Equivariant Flow-Based Sampling for Lattice Gauge Theory
Gurtej Kanwar1, Michael S Albergo2, Denis Boyda1
1Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Abstract:
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that, at small bare coupling, the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as hybrid Monte Carlo and heat bath.
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