Document Type


Date of Degree

Summer 2018

Degree Name

PhD (Doctor of Philosophy)

Degree In


First Advisor

Meurice, Yannick

First Committee Member

Osborn, James

Second Committee Member

Rodgers, Vincent

Third Committee Member

Polyzou, Wayne

Fourth Committee Member

Kletzing, Craig


In this work we explore how lattice gauge theory stands to benefit from new developments in machine learning, and look at two specific examples that illustrate this point. We begin with a brief overview of selected topics in machine learning for those who may be unfamiliar, and provide a simple example that helps to show how these ideas are carried out in practice.

After providing the relevant background information, we then introduce an example of renormalization group (RG) transformations, inspired by the tensor RG, that can be used for arbitrary image sets, and look at applying this idea to equilibrium configurations of the two-dimensional Ising model.

The second main idea presented in this thesis involves using machine learning to improve the efficiency of Markov Chain Monte Carlo (MCMC) methods. Explicitly, we describe a new technique for performing Hamiltonian Monte Carlo (HMC) simulations using an alternative leapfrog integrator that is parameterized by weights in a neural network. This work is based on the L2HMC ('Learning to Hamiltonian Monte Carlo') algorithm introduced in [1].


ising, lattice, machine learning, monte carlo, renormalization group, simulation


xiii, 183 pages


Includes bibliographical references (pages 182-183).


Copyright © 2018 Samuel Alfred Foreman

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