Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.
| Date | Stars |
|---|---|
| 2026-07-31 | 250 |
| 2026-08-05 | 251 |
| 2026-08-06 | 251 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# IDRLnet
[](https://www.apache.org/licenses/LICENSE-2.0)
[](https://python.org)
[](https://idrlnet.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/idrlnet)
[](https://hub.docker.com/r/idrl/idrlnet)
[](https://www.codefactor.io/repository/github/idrl-lab/idrlnet/overview/master)
**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>).
## Docs
- [Full docs](https://idrlnet.readthedocs.io/en/latest/)
- [Tutorial](https://idrlnet.readthedocs.io/en/latest/user/get_started/tutorial.html)
- Paper:
- IDRLnet: A Physics-Informed Neural Network Library. [arXiv](https://arxiv.org/abs/2107.04320)
## 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
Pull latest docker image from Dockerhub.
```bash
docker pull idrl/idrlnet:latest
docker run -it idrl/idrlnet:latest bash
```
Note: Available tags can be found in [Dockerhub](https://hub.docker.com/repository/docker/idrl/idrlnet).
### Anaconda
```bash
conda create -n idrlnet_dev python=3.8 -y
conda activate idrlnet_dev
pip install idrlnet
```
### 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.
- builds computational graph automatically;
- user-defined callbacks;
- solving variational minimization problem;
<img src="https://raw.githubusercontent.com/weipeng0098/picture/master/20210617082331.gif" alt="miniface" style="zoom:33%;" />
- 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 existiExcerpt of 4,834 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4e5ba331d429abb9, llm:Repository topics and description: 'Physics-Informed Neural Network (PINN)', 'pde-solver', 'inverse-problems', 'scientific-machine-learning', 'machine-learning', 'data-driven-model'. Purpose: Python toolbox for modeling and solving problems through PINNs.
matched fp:4e5ba331d429abb9, llm:Repository topics and description: 'Physics-Informed Neural Network (PINN)', 'pde-solver', 'inverse-problems', 'scientific-machine-learning', 'machine-learning', 'data-driven-model'. Purpose: Python toolbox for modeling and solving problems through PINNs.