{"id":25890894,"url":"https://github.com/mikediessner/nubo","last_synced_at":"2025-03-02T19:31:24.917Z","repository":{"id":153143441,"uuid":"607169202","full_name":"mikediessner/nubo","owner":"mikediessner","description":"NUBO is a Bayesian optimisation framework for the optimisation of expensive-to-evaluate black-box functions developed by the Fluid Dynamics Lab at Newcastle University.","archived":false,"fork":false,"pushed_at":"2024-03-11T15:04:35.000Z","size":17482,"stargazers_count":11,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-15T07:05:57.653Z","etag":null,"topics":["bayesian-optimisation","black-box-optimisation","derivative-free-optimisation","gaussian-processes","machine-learning","optimisation-algorithms"],"latest_commit_sha":null,"homepage":"https://nubopy.com","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mikediessner.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":"2023-02-27T13:09:52.000Z","updated_at":"2024-09-19T20:16:53.000Z","dependencies_parsed_at":"2024-02-04T14:02:17.183Z","dependency_job_id":null,"html_url":"https://github.com/mikediessner/nubo","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikediessner%2Fnubo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikediessner%2Fnubo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikediessner%2Fnubo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mikediessner%2Fnubo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mikediessner","download_url":"https://codeload.github.com/mikediessner/nubo/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241560225,"owners_count":19982384,"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":["bayesian-optimisation","black-box-optimisation","derivative-free-optimisation","gaussian-processes","machine-learning","optimisation-algorithms"],"created_at":"2025-03-02T19:30:28.746Z","updated_at":"2025-03-02T19:31:24.903Z","avatar_url":"https://github.com/mikediessner.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NUBO\n\nNUBO, short for Newcastle University Bayesian optimisation, is a Bayesian\noptimisation framework for the optimisation of expensive-to-evaluate black-box\nfunctions, such as physical experiments and computer simulations. It is\ndeveloped and maintained by the\n[Fluid Dynamics Lab](https://www.experimental-fluid-dynamics.com) at\n[Newcastle University](https://www.ncl.ac.uk). NUBO focuses primarily on\ntransparency and user experience to make Bayesian optimisation easily\naccessible to researchers from all disciplines. Transparency is ensured by\nclean and comprehensible code, precise references, and thorough documentation.\nUser experience is ensured by a modular and flexible design, easy-to-write\nsyntax, and careful selection of Bayesian optimisation algorithms. NUBO allows\nyou to tailor Bayesian optimisation to your specific problem by writing the\noptimisation loop yourself using the provided building blocks or using an\noff-the-shelf algorithm for common problems. Only algorithms and methods that\nare sufficiently tested and validated to perform well are included in NUBO. This\nensures that the package remains compact and does not overwhelm the user with an\nunnecessary large number of options. The package is written in\n[Python](https://www.python.org) but does not require expert knowledge of Python\nto optimise your simulations and experiments. NUBO is distributed as an\nopen-source software under the [BSD 3-Clause licence](https://joinup.ec.europa.eu/licence/bsd-3-clause-new-or-revised-license).\n\n \u003e Thanks for considering NUBO. If you have any questions, comments, or issues\n \u003e feel free to email us at m.diessner2@newcastle.ac.uk. Any feedback is highly\n \u003e appreciated and will help make NUBO better in the future.\n\n## Install NUBO\n\nInstall NUBO and all its dependencies directly from the\n[Python Package Index](https://pypi.org) *PyPI* using the\n[Python package manager](https://pip.pypa.io/en/latest/) *pip* with the\nfollowing code. We recommend the use of a virtual environment.S\n\n    pip install nubopy\n\n## Cite NUBO\n\nIf you are using NUBO for your research, please cite as:\n\n    Mike Diessner, Kevin J. Wilson, and Richard D. Whalley. \"NUBO: A Transparent Python Package for Bayesian Optimisation,\" arXiv preprint arXiv:2305.06709, 2023.\n\nIf you are using Bibtex, please cite as:\n\n```\n@article{diessner2023nubo,\n         title={NUBO: A Transparent Python Package for Bayesian Optimisation},\n         author={Diessner, Mike and Wilson, Kevin J and Whalley, Richard D},\n         journal={arXiv preprint arXiv:2305.06709},\n         year={2023}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikediessner%2Fnubo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmikediessner%2Fnubo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmikediessner%2Fnubo/lists"}