{"id":35159367,"url":"https://github.com/tudoroancea/math_591_project","last_synced_at":"2026-05-21T09:03:33.313Z","repository":{"id":172201731,"uuid":"648974520","full_name":"tudoroancea/math_591_project","owner":"tudoroancea","description":"Code for my project on 'Neural system identification and control for Formula Student Driverless cars'","archived":false,"fork":false,"pushed_at":"2024-02-07T17:38:01.000Z","size":19026,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-02-07T18:42:39.382Z","etag":null,"topics":["data-driven-control","deep-learning","model-predictive-control","neural-networks","system-identification"],"latest_commit_sha":null,"homepage":"","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/tudoroancea.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2023-06-03T11:34:12.000Z","updated_at":"2023-06-21T11:58:40.000Z","dependencies_parsed_at":null,"dependency_job_id":"53e6a8d0-7aac-4000-97bd-d239f6ba8954","html_url":"https://github.com/tudoroancea/math_591_project","commit_stats":null,"previous_names":["tudoroancea/math_591_project"],"tags_count":4,"template":false,"template_full_name":null,"purl":"pkg:github/tudoroancea/math_591_project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tudoroancea%2Fmath_591_project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tudoroancea%2Fmath_591_project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tudoroancea%2Fmath_591_project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tudoroancea%2Fmath_591_project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tudoroancea","download_url":"https://codeload.github.com/tudoroancea/math_591_project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tudoroancea%2Fmath_591_project/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33295256,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-21T02:57:32.698Z","status":"ssl_error","status_checked_at":"2026-05-21T02:57:31.990Z","response_time":62,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["data-driven-control","deep-learning","model-predictive-control","neural-networks","system-identification"],"created_at":"2025-12-28T17:52:52.734Z","updated_at":"2026-05-21T09:03:33.308Z","avatar_url":"https://github.com/tudoroancea.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Neural system identification and control for Formula Student Driverless cars\n\nThis repository contains the [report](OANCEA_TUDOR_DPC_project_report.pdf), \n[final presentation](MATH_591_final_presentation.pdf) and code for my first semester \nproject in my Computational Science and Engieering master's degree at EPFL.\n\nThe code contains the model definitions, training and evaluation scripts for the\nneural networks used in the project. Everything is written in Python 3.10 and is \nbased on Pytorch, Lightning Fabric and Wandb. It was designed to be very clear (although \npoorly documented), modular and extensible, and can be used for other projects\nas well. The data is available in the latest release of the repository.\n\n\u003c!-- ## Project description\n\n## Project results --\u003e\n\n## Codebase\n\n### Workspace setup\n\nTo setup the workspace you can run the following commands:\n```bash\ngit clone https://github.com/tudoroancea/math_591_project\ncd math_591_project\nmamba env create --file env.yml # or conda env create --file env.yml if you don't use mamba, but it's a shame not to use it since it's simply much MUCH faster\nconda activate math_591_project\nwget https://github.com/tudoroancea/math_591_project/releases/download/untagged-d3fb9058dc258922e9bc/dataset_v2.0.0.zip\nunzip dataset_v2.0.0.zip -d .\n```\nThen you can use the training scripts for the [system identification](train_sysid.py) and [control](train_control.py) tasks,\nand finally obtain again all the plots in my report using the evaluation scripts for the [system identification](sysid_experiments.py) and [control](control_experiments.py).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftudoroancea%2Fmath_591_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftudoroancea%2Fmath_591_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftudoroancea%2Fmath_591_project/lists"}