{"id":13569338,"url":"https://github.com/idrl-lab/idrlnet","last_synced_at":"2025-04-04T05:32:12.719Z","repository":{"id":41083213,"uuid":"383001207","full_name":"idrl-lab/idrlnet","owner":"idrl-lab","description":"IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.","archived":false,"fork":false,"pushed_at":"2023-06-30T08:47:43.000Z","size":795,"stargazers_count":177,"open_issues_count":5,"forks_count":53,"subscribers_count":11,"default_branch":"master","last_synced_at":"2024-03-14T16:43:52.984Z","etag":null,"topics":["data-driven-model","inverse-problems","machine-learning","pde-solver","physics-informed-neural-networks","python","scientific-machine-learning"],"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/idrl-lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2021-07-05T03:18:27.000Z","updated_at":"2024-03-11T18:48:10.000Z","dependencies_parsed_at":"2023-02-01T05:30:58.395Z","dependency_job_id":"937f473d-8720-4135-b136-51926b05c180","html_url":"https://github.com/idrl-lab/idrlnet","commit_stats":{"total_commits":40,"total_committers":3,"mean_commits":"13.333333333333334","dds":0.4,"last_synced_commit":"ac61391d371e0c0ba6d54a204134e718d1d0b90a"},"previous_names":[],"tags_count":9,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idrl-lab%2Fidrlnet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idrl-lab%2Fidrlnet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idrl-lab%2Fidrlnet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/idrl-lab%2Fidrlnet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/idrl-lab","download_url":"https://codeload.github.com/idrl-lab/idrlnet/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223100121,"owners_count":17087387,"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":["data-driven-model","inverse-problems","machine-learning","pde-solver","physics-informed-neural-networks","python","scientific-machine-learning"],"created_at":"2024-08-01T14:00:38.712Z","updated_at":"2024-11-05T01:32:11.042Z","avatar_url":"https://github.com/idrl-lab.png","language":"Python","funding_links":[],"categories":["Software","其他_机器学习与深度学习","Python"],"sub_categories":["Python"],"readme":"# IDRLnet\n\n[![License](https://img.shields.io/github/license/analysiscenter/pydens.svg)](https://www.apache.org/licenses/LICENSE-2.0)\n[![Python](https://img.shields.io/badge/python-3.7/3.8/3.9-blue.svg)](https://python.org)\n[![Documentation Status](https://readthedocs.org/projects/idrlnet/badge/?version=latest)](https://idrlnet.readthedocs.io/en/latest/?badge=latest)\n[![PyPI version](https://badge.fury.io/py/idrlnet.svg)](https://badge.fury.io/py/idrlnet)\n[![DockerHub](https://img.shields.io/docker/pulls/idrl/idrlnet.svg)](https://hub.docker.com/r/idrl/idrlnet)\n[![CodeFactor](https://www.codefactor.io/repository/github/idrl-lab/idrlnet/badge/master)](https://www.codefactor.io/repository/github/idrl-lab/idrlnet/overview/master)\n\n**IDRLnet** is a machine learning library on top of [PyTorch](https://pytorch.org/). Use IDRLnet if you need a machine learning library that solves both forward and inverse differential equations via physics-informed neural networks (PINN). IDRLnet is a flexible framework inspired by [Nvidia Simnet](https://developer.nvidia.com/simnet\u003e).\n\n## Docs\n\n- [Full docs](https://idrlnet.readthedocs.io/en/latest/)\n- [Tutorial](https://idrlnet.readthedocs.io/en/latest/user/get_started/tutorial.html)\n- Paper:\n   - IDRLnet: A Physics-Informed Neural Network Library. [arXiv](https://arxiv.org/abs/2107.04320)\n\n## Installation\n\nChoose one of the following installation methods.\n\n### PyPI\n\nSimple installation from PyPI.\n\n```bash\npip install -U idrlnet\n```\n\nNote: To avoid version conflicts, please use some tools to create a virtual environment first.\n\n### Docker\n\nPull latest docker image from Dockerhub.\n\n```bash\ndocker pull idrl/idrlnet:latest\ndocker run -it idrl/idrlnet:latest bash\n\n```\n\nNote: Available tags can be found in [Dockerhub](https://hub.docker.com/repository/docker/idrl/idrlnet).\n\n### Anaconda\n\n```bash\nconda create -n idrlnet_dev python=3.8 -y\nconda activate idrlnet_dev\npip install idrlnet\n```\n\n### From Source\n\n```\ngit clone https://github.com/idrl-lab/idrlnet\ncd idrlnet\npip install -e .\n```\n\n\n## Features\n\nIDRLnet supports\n\n-  complex domain geometries without mesh generation. Provided geometries include interval, triangle, rectangle, polygon, circle, sphere... Other geometries can be constructed using three boolean operations: union, difference, and intersection;\n   ![Geometry](https://raw.githubusercontent.com/weipeng0098/picture/master/20210617081809.png)\n   \n- sampling in the interior of the defined geometry or on the boundary with given conditions.\n\n- enables the user code to be structured. Data sources, operations, constraints are all represented by ``Node``. The graph will be automatically constructed via label symbols of each node. Getting rid of the explicit construction via explicit expressions, users model problems more naturally.\n\n- builds computational graph automatically;\n\n   ![computationDomain](https://raw.githubusercontent.com/weipeng0098/picture/master/20220815142531.png)\n\n-  user-defined callbacks;\n   \n   ![callback](https://raw.githubusercontent.com/weipeng0098/picture/master/20220815142621.png)\n   \n-  solving variational minimization problem;\n   \u003cimg src=\"https://raw.githubusercontent.com/weipeng0098/picture/master/20210617082331.gif\" alt=\"miniface\" style=\"zoom:33%;\" /\u003e\n   \n- solving integral differential equation;\n\n- adaptive resampling;\n\n-  recover unknown parameters of PDEs from noisy measurement data.\n\nIt is also easy to customize IDRLnet to meet new demands.\n\n-  Main Dependencies\n\n    -  [Matplotlib](https://matplotlib.org/)\n    -  [NumPy](http://www.numpy.org/)\n    -  [Sympy](https://https://www.sympy.org/)==1.5.1\n    -  [pytorch](https://www.tensorflow.org/)\u003e=1.7.0\n\n## Contributing to IDRLnet\n\nFirst off, thanks for taking the time to contribute!\n\n-  **Reporting bugs.** To report a bug, simply open an issue in the GitHub \"Issues\" section.\n\n-  **Suggesting enhancements.** To submit an enhancement suggestion for IDRLnet, including completely new features and minor improvements to existing functionality, let us know by opening an issue.\n   \n-  **Pull requests.** If you made improvements to IDRLnet, fixed a bug, or had a new example, feel free to send us a pull-request.\n   \n-  **Asking questions.** To get help on how to use IDRLnet or its functionalities, you can as well open an issue.\n\n-  **Answering questions.** If you know the answer to any question in the \"Issues\", you are welcomed to answer.\n\n## The Team\n\nIDRLnet was originally developed by IDRL lab.\n\n## Citation\nFeel free to cite this library.\n\n```bibtex\n@article{peng2021idrlnet,\n      title={IDRLnet: A Physics-Informed Neural Network Library}, \n      author={Wei Peng and Jun Zhang and Weien Zhou and Xiaoyu Zhao and Wen Yao and Xiaoqian Chen},\n      year={2021},\n      eprint={2107.04320},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fidrl-lab%2Fidrlnet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fidrl-lab%2Fidrlnet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fidrl-lab%2Fidrlnet/lists"}