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idrlnet
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@ -7,10 +7,7 @@
[![DockerHub](https://img.shields.io/docker/pulls/idrl/idrlnet.svg)](https://hub.docker.com/r/idrl/idrlnet)
[![CodeFactor](https://www.codefactor.io/repository/github/idrl-lab/idrlnet/badge/master)](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>).
**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
@ -66,21 +63,27 @@ pip install -e .
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;
- 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;
![Geometry](https://raw.githubusercontent.com/weipeng0098/picture/master/20210617081809.png)
- sampling in the interior of the defined geometry or on the boundary with given conditions.
- 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.
- 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;
![computationDomain](https://raw.githubusercontent.com/weipeng0098/picture/master/20220815142531.png)
- user-defined callbacks;
![callback](https://raw.githubusercontent.com/weipeng0098/picture/master/20220815142621.png)
- solving variational minimization problem;
<img src="https://raw.githubusercontent.com/weipeng0098/picture/master/20210617082331.gif" alt="miniface" style="zoom:33%;" />
- solving integral differential equation;
- solving integral differential equation;
- adaptive resampling;
- adaptive resampling;
- recover unknown parameters of PDEs from noisy measurement data.
@ -99,11 +102,9 @@ 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.
- **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.
- **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.