{"id":16916742,"url":"https://github.com/ternaus/machine_learning_sign","last_synced_at":"2026-05-17T13:02:09.326Z","repository":{"id":28673901,"uuid":"32193650","full_name":"ternaus/machine_learning_sign","owner":"ternaus","description":"Machine learning as a way to overcome sign problem.","archived":false,"fork":false,"pushed_at":"2015-03-15T00:29:59.000Z","size":220,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-25T21:13:17.833Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ternaus.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-03-14T03:13:46.000Z","updated_at":"2015-03-15T00:29:59.000Z","dependencies_parsed_at":"2022-08-24T10:10:49.991Z","dependency_job_id":null,"html_url":"https://github.com/ternaus/machine_learning_sign","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/ternaus%2Fmachine_learning_sign","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ternaus%2Fmachine_learning_sign/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ternaus%2Fmachine_learning_sign/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ternaus%2Fmachine_learning_sign/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ternaus","download_url":"https://codeload.github.com/ternaus/machine_learning_sign/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244717362,"owners_count":20498283,"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":"2024-10-13T19:29:39.136Z","updated_at":"2026-05-17T13:02:04.257Z","avatar_url":"https://github.com/ternaus.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Extrapolation to the region with a sign problem.\n\n## Introduction\n\nSign problem is a fundamential problem in a theoretical condensed matter physics that pervents theorist from numerical investigation properties of the fermionic Hubbard model. There is no clear way how to measure quantities of interest in a region where average sign in the monte carlo simulations is close to zero, but I will try to use machine learning algorithms to create a model that will be able to extrapolate to the regions of interest.\n\nSign problem will not appear at:\n * mu = 0 This corresponds to a half filled case, in which sign = 1 protected by the particle symmatric form of the Hamiltonian.\n * U = 0 In this case there is no sign problem and solution to the problem can be found analytically.\n\nIn all other cases:\n\nSign will appear at:\n * U -\u003e infinity\n * beta -\u003e infinity\n\n\n## What is my training data?\n\nAs a training set I will use output of the Determinant Monte Carlo simulations generated by QUEST package and exact diagonalization results generated by the ALPS package.\n\n### What lattice I am working with?\n\nIn this project I will work only with the square lattice, although other lattices like honeycomb,triangular, Kagome, Lieb and others may be investigated later.\n\nI will imply periodic boundary conditions on the lattice and I will not use lattices with sizes less than 4 in any direction.\n\n### What are my input parameters?\n\nAs an input parameters I will use:\n\n* Nx - Number of sites in the x direction. (even numbers only to avoid frustration)\n* Ny - Number of sites in the y direction. (even numbers only to avoid frustration)\n* mu - chemical potential\n* beta - inverse temperature\n* U - interaction strength\n\n### What data do we have?\n\nExact diagonalization will give you results for any mu and U, but for (Nx, Ny) = (4, 4) and T = 0 (beta = infinity)\n\nQuantum Monte Carlo data will be give for different U, beta, mu, Nx, Ny. Data files where \u003csign\u003e \u003c 0.1 will be considered bad (not reliable) and they will be exluded from the consideration.\n\n\n### What algorithms will I use?\n\nI am planning on using all regression algorithms from the scikit-learn package.\nComparison of their efficiency will be also provided.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fternaus%2Fmachine_learning_sign","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fternaus%2Fmachine_learning_sign","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fternaus%2Fmachine_learning_sign/lists"}