Metadata-Version: 2.1
Name: idrlnet
Version: 0.0.1rc3
Summary: IDRLnet
Home-page: https://github.com/idrl-lab/idrlnet
Author: Intelligent Design & Robust Learning lab
Author-email: weipeng@deepinfar.cn
License: UNKNOWN
Description: [![License](https://img.shields.io/github/license/analysiscenter/pydens.svg)](https://www.apache.org/licenses/LICENSE-2.0)
        [![Python](https://img.shields.io/badge/python-3.8-blue.svg)](https://python.org)
        [![Documentation Status](https://readthedocs.org/projects/idrlnet/badge/?version=latest)](https://idrlnet.readthedocs.io/en/latest/?badge=latest)
        
        # IDRLnet
        
        
        **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>).
        
        ## Installation
        
        Choose one of the following installation methods.
        
        ### PyPI
        
        Simple installation from PyPI
        
        ```bash
        pip install -U idrlnet
        ```
        
        Note: To avoid version conflicts, please use some tools to create a virtual environment first.
        
        ### Docker
        
        ```bash
        docker pull idrl/idrlnet:latest
        ```
        
        ### Anaconda
        
        
        
        ### From Source
        
        ```
        git clone https://github.com/idrl-lab/idrlnet
        cd idrlnet
        pip install -e .
        ```
        
        
        ## Features
        
        IDRLnet supports
        
        -  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;
        
        -  sampling in the interior of the defined geometry or on the boundary with given conditions.
        
        -  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.
        
        -  solving variational minimization problem;
        
        -  solving integral differential equation;
        
        -  adaptive resampling;
        
        -  recover unknown parameters of PDEs from noisy measurement data.
        
        It is also easy to customize IDRLnet to meet new demands.
        
        -  Main Dependencies
        
            -  [Matplotlib](https://matplotlib.org/)
            -  [NumPy](http://www.numpy.org/)
            -  [Sympy](https://https://www.sympy.org/)==1.5.1
            -  [pytorch](https://www.tensorflow.org/)>=1.7.0
        
        ## Contributing to IDRLnet
        
        First off, thanks for taking the time to contribute!
        
        -  **Reporting bugs.** To report a bug, simply open an issue in the GitHub "Issues" section.
        
        -  **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.
        
        -  **Pull requests.** If you made improvements to IDRLnet, fixed a bug, or had a new example, feel free to send us a
           pull-request.
        
        -  **Asking questions.** To get help on how to use IDRLnet or its functionalities, you can as well open an issue.
        
        -  **Answering questions.** If you know the answer to any question in the "Issues", you are welcomed to answer.
        
        ## The Team
        
        IDRLnet was originally developed by IDRL lab.
        
        ## Citation
        Feel free to cite this library.
        
        ```bibtex
        @article{peng2021idrlnet,
              title={IDRLnet: A Physics-Informed Neural Network Library}, 
              author={Wei Peng and Jun Zhang and Weien Zhou and Xiaoyu Zhao and Wen Yao and Xiaoqian Chen},
              year={2021},
              eprint={2107.04320},
              archivePrefix={arXiv},
              primaryClass={cs.LG}
        }
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
