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DeepXDE

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DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms:

DeepXDE supports five tensor libraries as backends: TensorFlow 1.x (tensorflow.compat.v1 in TensorFlow 2.x), TensorFlow 2.x, PyTorch, JAX, and PaddlePaddle. For how to select one, see Working with different backends.

Documentation: ReadTheDocs

Features

DeepXDE has implemented many algorithms as shown above and supports many features:

All the components of DeepXDE are loosely coupled, and thus DeepXDE is well-structured and highly configurable. It is easy to customize DeepXDE to meet new demands.

Installation

DeepXDE requires one of the following backend-specific dependencies to be installed:

Then, you can install DeepXDE itself.

$ pip install deepxde
$ conda install -c conda-forge deepxde
$ git clone https://github.com/lululxvi/deepxde.git

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Cite DeepXDE

If you use DeepXDE for academic research, you are encouraged to cite the following paper:

@article{lu2021deepxde,
  author  = {Lu, Lu and Meng, Xuhui and Mao, Zhiping and Karniadakis, George Em},
  title   = {{DeepXDE}: A deep learning library for solving differential equations},
  journal = {SIAM Review},
  volume  = {63},
  number  = {1},
  pages   = {208-228},
  year    = {2021},
  doi     = {10.1137/19M1274067}
}

Contributing to DeepXDE

First off, thanks for taking the time to contribute!

Slack. The DeepXDE Slack hosts a primary audience of moderate to experienced DeepXDE users and developers for general chat, online discussions, collaboration, etc. If you need a slack invite, please send me an email.

The Team

DeepXDE was developed by Lu Lu under the supervision of Prof. George Karniadakis at Brown University from the summer of 2018 to 2020. DeepXDE was originally self-hosted in Subversion at Brown University, under the name SciCoNet (Scientific Computing Neural Networks). On Feb 7, 2019, SciCoNet was moved from Subversion to GitHub, renamed to DeepXDE.

DeepXDE is currently maintained by Lu Lu at Yale University with major contributions coming from many talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: Paul Escapil-Inchauspé, Zongren Zou, Jialin Li, Saransh Chopra, Sensen He, Vladimir Dudenkov, Anran Jiao, Zhongyi Jiang, Shunyuan Mao.

License

LGPL-2.1 License