[![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)

## Installation

### Docker

```bash
git clone https://git.idrl.site/pengwei/idrlnet_public
cd idrlnet_public
docker build . -t idrlnet_dev
docker run -it -p [EXPOSED_SSH_PORT]:22 -v [CURRENT_WORK_DIR]:/root/pinnnet idrlnet_dev:latest bash
```

### Anaconda

```bash
git clone https://git.idrl.site/pengwei/idrlnet_public
cd idrlnet_public
conda create -n idrlnet_dev python=3.8 -y
conda activate idrlnet_dev
pip install -r requirements.txt
pip install -e .
```

# IDRLnet

IDRLnet is a machine learning library on top of [Pytorch](https://www.tensorflow.org/). Use IDRLnet if you need a machine
learning library that solves both forward and inverse partial differential equations (PDEs) via physics-informed neural
networks (PINN). IDRLnet is a flexible framework inspired by [Nvidia Simnet](https://developer.nvidia.com/simnet>).

## 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.