{"id":13870166,"url":"https://github.com/saforem2/thesis","last_synced_at":"2026-01-05T21:03:03.753Z","repository":{"id":117904531,"uuid":"191398647","full_name":"saforem2/thesis","owner":"saforem2","description":"PhD Thesis","archived":false,"fork":false,"pushed_at":"2019-08-26T22:41:28.000Z","size":25447,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-18T22:57:50.392Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/saforem2.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-06-11T15:20:22.000Z","updated_at":"2021-01-08T19:12:23.000Z","dependencies_parsed_at":"2024-01-16T08:04:05.692Z","dependency_job_id":null,"html_url":"https://github.com/saforem2/thesis","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saforem2%2Fthesis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saforem2%2Fthesis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saforem2%2Fthesis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saforem2%2Fthesis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/saforem2","download_url":"https://codeload.github.com/saforem2/thesis/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245045555,"owners_count":20552056,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-05T20:01:32.515Z","updated_at":"2026-01-05T21:02:58.724Z","avatar_url":"https://github.com/saforem2.png","language":"TeX","funding_links":[],"categories":["TeX"],"sub_categories":[],"readme":"# Learning Better Physics: A Machine Learning Approach to Lattice Gauge Theory\n\n## Info\nAuthor: Sam Foreman\n\nSubmitted in partial fufillment of the requirements for the Doctor of\nPhilosophy degree in Physics in the Graduate College of The University of Iowa\n\nAugust 2019\n\nThesis Supervisor: Yannick Meurice\n\n## Abstract\nIn this work we explore how lattice gauge theory stands to benefit from new\ndevelopments in machine learning, and look at two specific examples that\nillustrate this point.\n%\nWe begin with a brief overview of selected topics in machine learning for those\nwho may be unfamiliar, and provide a simple example that helps to show how\nthese ideas are carried out in practice.\n\nAfter providing the relevant background information, we then introduce an\nexample of renormalization group (RG) transformations, inspired by the tensor\nRG, that can be used for arbitrary image sets, and look at applying this idea\nto equilibrium configurations of the two-dimensional Ising model.\n\nThe second main idea presented in this thesis involves using machine learning\nto improve the efficiency of Markov Chain Monte Carlo (MCMC) methods.\n%\nExplicitly, we describe a new technique for performing Hamiltonian Monte Carlo\n(HMC) simulations using an alternative leapfrog integrator that is\nparameterized by weights in a neural network.\n%\nThis work is based on the L2HMC [`Learning to Hamiltonian Monte\nCarlo'](https://arxiv.org/abs/1711.09268)\nalgorithm.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaforem2%2Fthesis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaforem2%2Fthesis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaforem2%2Fthesis/lists"}