{"id":17091707,"url":"https://github.com/ascillitoe/mondrian_turbulence","last_synced_at":"2026-04-25T16:34:54.415Z","repository":{"id":114887356,"uuid":"245200082","full_name":"ascillitoe/mondrian_turbulence","owner":"ascillitoe","description":"A python toolset to augment RANS models with LES/DNS data, using Random or Mondrian forests.","archived":false,"fork":false,"pushed_at":"2021-01-12T16:51:43.000Z","size":112,"stargazers_count":3,"open_issues_count":0,"forks_count":1,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-03-23T16:34:08.792Z","etag":null,"topics":["cfd","machine-learning","turbulence-modelling"],"latest_commit_sha":null,"homepage":"https://doi.org/10.1016/j.jcp.2021.110116","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ascillitoe.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-03-05T15:31:26.000Z","updated_at":"2024-12-22T17:36:41.000Z","dependencies_parsed_at":null,"dependency_job_id":"c695064c-80ff-4402-a80b-d3e26432a94c","html_url":"https://github.com/ascillitoe/mondrian_turbulence","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ascillitoe/mondrian_turbulence","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascillitoe%2Fmondrian_turbulence","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascillitoe%2Fmondrian_turbulence/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascillitoe%2Fmondrian_turbulence/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascillitoe%2Fmondrian_turbulence/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ascillitoe","download_url":"https://codeload.github.com/ascillitoe/mondrian_turbulence/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ascillitoe%2Fmondrian_turbulence/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32269462,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-25T09:15:33.318Z","status":"ssl_error","status_checked_at":"2026-04-25T09:15:31.997Z","response_time":59,"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":["cfd","machine-learning","turbulence-modelling"],"created_at":"2024-10-14T13:59:22.453Z","updated_at":"2026-04-25T16:34:54.399Z","avatar_url":"https://github.com/ascillitoe.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Data-driven turbulence modelling with random and Mondrian forests\n\nThis python package trains random forests and Mondrian forests on high fidelity LES/DNS data. The trained models can then be used to predict turbulence parameters for a new RANS flowfield. For more details see:\n\nAshley Scillitoe, Pranay Seshadri, Mark Girolami,\n*Uncertainty quantification for data-driven turbulence modelling with mondrian forests*,\nJournal of Computational Physics,\n2021,\n110116,\nISSN 0021-9991,\ndoi: [10.1016/j.jcp.2021.110116](https://doi.org/10.1016/j.jcp.2021.110116). \narXiv: [2003.01968](http://arxiv.org/abs/2003.01968).\n\n### How to use\nInstructions and examples coming soon!\n\n### Notes\n* Regressors and classifiers are implemented, however the classifer code is out of date and should be used with caution!\n* requirements.txt file to enable easy installation is in the works.\n\n### Key dependencies\n* `scikit-learn`: For Random forest classifier and regressor - https://scikit-learn.org/stable/\n* `scikit-garden`: For Mondrian forest regressor - https://scikit-garden.github.io\n* `pyvista`: For reading and writing vtk files, and built into the `CaseData` class - https://github.com/pyvista/pyvista\n* `forestci`: For calculating infinitesimal jackknife uncertainty estimates for random forests - https://github.com/scikit-learn-contrib/forest-confidence-interval\n* `shap`: For calculating SHAP values - https://github.com/ascillitoe/shap (forked from https://github.com/slundberg/shap)\n* `eli5`: For calculating permutation importance - https://github.com/TeamHG-Memex/eli5\n\n### Acknowledgments\nThis work was supported by wave 1 of The UKRI Strategic Priorities Fund under the EPSRC grant EP/T001569/1, particularly the [Digital Twins in Aeronautics](https://www.turing.ac.uk/research/research-projects/digital-twins-aeronautics) theme within that grant, and [The Alan Turing Institute](https://www.turing.ac.uk).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fascillitoe%2Fmondrian_turbulence","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fascillitoe%2Fmondrian_turbulence","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fascillitoe%2Fmondrian_turbulence/lists"}