Skip to content
Deep learning library for solving differential equations
Branch: master
Clone or download
Latest commit 943f75f Jul 12, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
deepxde Sort x for plot; Add print info to save Jul 12, 2019
docs Bump to v0.2.0 Jul 12, 2019
examples Add more examples Jul 12, 2019
.codacy.yml Codacy ignore README and docs Jun 25, 2019
.gitignore Update docstring Jun 25, 2019
.readthedocs.yml Update docs Jun 25, 2019
.travis.yml Use xenial for Python 3.7 Jun 24, 2019
LICENSE Initial commit Feb 8, 2019
MANIFEST.in Update docs Jun 25, 2019
README.md Add paper Jul 11, 2019
requirements.txt Update docs Jun 25, 2019
setup.cfg Add setup Jun 11, 2019
setup.py Bump to v0.2.0 Jul 12, 2019

README.md

DeepXDE ℒ

Build Status Documentation Status Codacy Badge PyPI Version Conda Version License

DeepXDE is a deep learning library for solving differential equations on top of TensorFlow.

Use DeepXDE if you need a deep learning library that

  • solves partial differential equations (PDEs),
  • solves integro-differential equations (IDEs),
  • solves fractional partial differential equations (fPDEs),
  • solves inverse problems for differential equations,
  • approximates functions from a dataset with/without constraints,
  • approximates functions from multi-fidelity data.

DeepXDE is extensible to solve other problems in Scientific Machine Learning.

Documentation: ReadTheDocs

DeepXDE Paper: arXiv

Features

DeepXDE supports

  • complex domain geometries without tyranny mesh generation. The primitive geometries are interval, triangle, rectangle, polygon, disk, cuboid, and sphere. Other geometries can be constructed as constructive solid geometry (CSG) using three boolean operations: union, difference, and intersection;
  • multi-physics, i.e., coupled PDEs;
  • 4 types of boundary conditions: Dirichlet, Neumann, Robin, and periodic;
  • time-dependent PDEs are solved as easily as time-independent ones by only adding initial conditions;
  • residual-based adaptive refinement (RAR);
  • uncertainty quantification using dropout;
  • two types of neural networks: fully connected neural network, and residual neural network;
  • many different losses, metrics, optimizers, learning rate schedules, initializations, regularizations, etc.;
  • useful techniques, such as dropout and batch normalization;
  • callbacks to monitor the internal states and statistics of the model during training;
  • enables the user code to be compact, resembling closely the mathematical formulation.

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 TensorFlow to be installed. Then, you can install DeepXDE itself.

  • Install the stable version with pip:
$ pip install deepxde
  • Install the stable version with conda:
$ conda install -c conda-forge deepxde
  • For developers, you should clone the folder to your local machine and put it along with your project scripts.
$ git clone https://github.com/lululxvi/deepxde.git

Cite DeepXDE

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

@article{lu2019deepxde,
    author  = {Lu, Lu and Meng, Xuhui and Mao, Zhiping and Karniadakis, George E.},
    title   = {{DeepXDE}: A deep learning library for solving differential equations},
    journal = {arXiv preprint arXiv:1907.04502},
    year    = {2019}
}

Why this logo, ℒ?

The art of Scientific Machine Learning with deep learning is to design Loss ℒ.

License

Apache license 2.0

You can’t perform that action at this time.