{"id":19932085,"url":"https://github.com/amazon-science/operator-probconserv","last_synced_at":"2026-03-14T20:39:22.722Z","repository":{"id":228495583,"uuid":"762088330","full_name":"amazon-science/operator-probconserv","owner":"amazon-science","description":"Official implementation of Operator-ProbConserv: OOD UQ for Neural Operators","archived":false,"fork":false,"pushed_at":"2024-06-14T19:06:02.000Z","size":40,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":4,"default_branch":"main","last_synced_at":"2024-06-14T20:26:25.421Z","etag":null,"topics":["conservation-laws","neural-operators","out-of-domain","partial-differential-equations","shock-capturing","uncertainty-quantification"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/amazon-science.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2024-02-23T03:59:35.000Z","updated_at":"2024-06-14T19:06:06.000Z","dependencies_parsed_at":"2024-03-19T03:51:50.957Z","dependency_job_id":null,"html_url":"https://github.com/amazon-science/operator-probconserv","commit_stats":null,"previous_names":["amazon-science/operator-probconserv"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amazon-science%2Foperator-probconserv","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amazon-science%2Foperator-probconserv/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amazon-science%2Foperator-probconserv/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/amazon-science%2Foperator-probconserv/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/amazon-science","download_url":"https://codeload.github.com/amazon-science/operator-probconserv/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224360233,"owners_count":17298319,"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":["conservation-laws","neural-operators","out-of-domain","partial-differential-equations","shock-capturing","uncertainty-quantification"],"created_at":"2024-11-12T23:08:59.402Z","updated_at":"2026-03-14T20:39:22.681Z","avatar_url":"https://github.com/amazon-science.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Operator-ProbConserv: OOD Uncertainty Quantification (UQ) for Neural Operators\n\nThis repository contains the code for the paper \"Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs\" by S. Chandra Mouli, Danielle C. Maddix, Shima Alizadeh, Gaurav Gupta, Yuyang Wang, Andrew Stuart, Michael W. Mahoney\n\n## Setup\nInstall dependencies by running\n```\nconda create -n env python=3.9\nconda activate env\npip install -r requirements.txt\n```\n\nRun `python -u experiment_ood_params.py --help` for possible options.\n\nExample to train DiverseNO model on 1-d heat equation task:\n```\npython -u experiment_ood_params.py --model=DiverseFNO2d --dataset=HeatEquation_1D --seed=0 --dataset_params=1,5,0,0 --train_ood_dataset_params=1,5,0,0 --n_samples=200 --tplot=0.5 --m.n_models=10 --m.reg_type=weights_l2 --m.reg_strength=1 --epochs=1000\n```\n\nTo evaluate the trained model on different OOD parameters, use `--ood_dataset_params` and `--no_train` options.\n```\npython -u experiment_ood_params.py --model=DiverseFNO2d --dataset=HeatEquation_1D --seed=0 --dataset_params=1,5,0,0 --train_ood_dataset_params=1,5,0,0 --n_samples=200 --tplot=0.5 --m.n_models=10 --m.reg_type=weights_l2 --m.reg_strength=1 --epochs=1000 --ood_dataset_params=5,6,0,0 --no_train\n```\n\nModels: EnsembleFNO2d, BayesianFNO2d, MCDropoutFNO2d, OutputVarFNO2d, DiverseFNO2d\nDatasets: HeatEquation_1D, PME_1D, StefanPME_1D, LinearAdvection_1D.\n\n## Sources\nThis repo contains modified versions of the code found in the following repos:\n\nhttps://github.com/zongyi-li/fourier_neural_operator: For implementation of the Fourier Neural Operator (FNO) (MIT license)\n\nhttps://github.com/amazon-science/probconserv: For implementation of ProbConserv (Apache 2.0 license)\n\n## Citation\nIf you use this code, or our work, please cite: \n\n```\n@inproceedings{mouli2024_ood_uq_no,\n    title={Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs},\n    author={Mouli, S.C., Maddix, D.C., Alizadeh, S., Gupta, G., Stuart, A., Mahoney, M.W., Wang, Y.},\n    booktitle={International Conference on Machine Learning},\n    volume = {235},\n    organization={PMLR},  \n    year={2024}\n}\n```\n\n## Security\n\nSee [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.\n\n## License\n\nThis project is licensed under the Apache-2.0 License.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famazon-science%2Foperator-probconserv","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famazon-science%2Foperator-probconserv","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famazon-science%2Foperator-probconserv/lists"}