forked from idrl/idrlnet
130 lines
4.7 KiB
Markdown
130 lines
4.7 KiB
Markdown
# IDRLnet
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[](https://www.apache.org/licenses/LICENSE-2.0)
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[](https://python.org)
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[](https://idrlnet.readthedocs.io/en/latest/?badge=latest)
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[](https://badge.fury.io/py/idrlnet)
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[](https://hub.docker.com/r/idrl/idrlnet)
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[](https://www.codefactor.io/repository/github/idrl-lab/idrlnet/overview/master)
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**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>).
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## Docs
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- [Full docs](https://idrlnet.readthedocs.io/en/latest/)
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- [Tutorial](https://idrlnet.readthedocs.io/en/latest/user/get_started/tutorial.html)
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- Paper:
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- IDRLnet: A Physics-Informed Neural Network Library. [arXiv](https://arxiv.org/abs/2107.04320)
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## Installation
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Choose one of the following installation methods.
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### PyPI
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Simple installation from PyPI.
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```bash
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pip install -U idrlnet
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```
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Note: To avoid version conflicts, please use some tools to create a virtual environment first.
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### Docker
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Pull latest docker image from Dockerhub.
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```bash
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docker pull idrl/idrlnet:latest
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docker run -it idrl/idrlnet:latest bash
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```
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Note: Available tags can be found in [Dockerhub](https://hub.docker.com/repository/docker/idrl/idrlnet).
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### Anaconda
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```bash
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conda create -n idrlnet_dev python=3.8 -y
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conda activate idrlnet_dev
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pip install idrlnet
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```
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### From Source
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```
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git clone https://github.com/idrl-lab/idrlnet
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cd idrlnet
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pip install -e .
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```
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## Features
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IDRLnet supports
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- 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;
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- sampling in the interior of the defined geometry or on the boundary with given conditions.
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- 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.
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- builds computational graph automatically;
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- user-defined callbacks;
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- solving variational minimization problem;
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<img src="https://raw.githubusercontent.com/weipeng0098/picture/master/20210617082331.gif" alt="miniface" style="zoom:33%;" />
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- solving integral differential equation;
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- adaptive resampling;
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- recover unknown parameters of PDEs from noisy measurement data.
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It is also easy to customize IDRLnet to meet new demands.
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- Main Dependencies
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- [Matplotlib](https://matplotlib.org/)
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- [NumPy](http://www.numpy.org/)
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- [Sympy](https://https://www.sympy.org/)==1.5.1
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- [pytorch](https://www.tensorflow.org/)>=1.7.0
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## Contributing to IDRLnet
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First off, thanks for taking the time to contribute!
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- **Reporting bugs.** To report a bug, simply open an issue in the GitHub "Issues" section.
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- **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.
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- **Pull requests.** If you made improvements to IDRLnet, fixed a bug, or had a new example, feel free to send us a pull-request.
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- **Asking questions.** To get help on how to use IDRLnet or its functionalities, you can as well open an issue.
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- **Answering questions.** If you know the answer to any question in the "Issues", you are welcomed to answer.
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## The Team
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IDRLnet was originally developed by IDRL lab.
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## Citation
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Feel free to cite this library.
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```bibtex
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@article{peng2021idrlnet,
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title={IDRLnet: A Physics-Informed Neural Network Library},
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author={Wei Peng and Jun Zhang and Weien Zhou and Xiaoyu Zhao and Wen Yao and Xiaoqian Chen},
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year={2021},
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eprint={2107.04320},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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```
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