Metadata-Version: 2.1
Name: GreenLearning
Version: 1.0
Summary: Deep learning library for learning Green's functions
Home-page: https://github.com/NBoulle/greenlearning
Author: Nicolas Boulle
Author-email: boulle@maths.ox.ac.uk
License: Apache-2.0
Download-URL: https://github.com/NBoulle/greenlearning/tarball/v1.0
Description: # GreenLearning
        
        [![Documentation Status](https://readthedocs.org/projects/greenlearning/badge/?version=latest)](https://greenlearning.readthedocs.io)
        
        GreenLearning is a deep learning library based on Tensorflow for learning [Green's functions](https://en.wikipedia.org/wiki/Green%27s_function) associated with [partial differential operators](https://en.wikipedia.org/wiki/Differential_operator).
        
        | ![laplace_rational.png](docs/images/laplace.png) | 
        |:--:| 
        | *Exact and learned Green’s function of the Laplace operator..* |
        
        Below is an example of the Green's function of a second-order differential operator with variable coefficients learned by `GreenLearning`.
        
        | ![laplace_rational.png](docs/images/variable_coeffs_rational.png) | 
        |:--:| 
        | *Learned Green’s function of a second order ODE with variable coefficients.* |
        
        See [https://greenlearning.readthedocs.io/en/latest/gallery.html](https://greenlearning.readthedocs.io/en/latest/gallery.html) for more examples.
        
        The library is maintained by [Nicolas Boullé](https://people.maths.ox.ac.uk/boulle/). If you are interested in using it, do not hesitate to get in contact with him at `boulle@maths.ox.ac.uk`.
        
        **Documentation**: [ReadTheDocs](https://greenlearning.readthedocs.io/)
        
        ## Features
        
        - GreenLearning learns Green's functions and homogeneous solutions associated with scalar and systems of linearized partial differential equations in 1D and 2D with deep learning.
        - [Rational neural networks](https://proceedings.neurips.cc/paper/2020/file/a3f390d88e4c41f2747bfa2f1b5f87db-Paper.pdf) are implemented and used to increase the accuracy of the learned Green's functions.
        - GreenLearning requires no hyperparameter tuning to successfully learn Green's functions.
        - The neural networks can be created and trained easily with a few lines of code.
        - It is simple to generate the training datasets with MATLAB scripts.
        
        ## Installation
        
        ### Requirements
        
        GreenLearning relies on the following Python libraries:
        
        - [TensorFlow](https://www.tensorflow.org/) >= 1.15.0
        - [Matplotlib](https://matplotlib.org/)
        - [NumPy](http://www.numpy.org/)
        - [SciPy](https://www.scipy.org/)
        
        ### How to install GreenLearning
        
        - For users, you can install the stable version with `pip`:
        ```
        pip install greenlearning
        ```
        or with `conda`:
        ```
        conda install -c conda-forge greenlearning
        ```
        
        - For developers, you should clone the GitHub repository and install it manually on your machine::
        ```
        git clone https://github.com/NBoulle/greenlearning.git
        cd greenlearning
        pip install -e.
        ```
        
        ## Citation
        Please cite the following papers if you are using GreenLearning.
        
        - About GreenLearning:
        ```
        @article{boulle2021data,
        title={Data-driven discovery of physical laws with human-understandable deep learning},
        author={Boull{\'e}, Nicolas and Earls, Christopher J. and Townsend, Alex,
        journal={arXiv preprint arXiv:},
        year={2021}
        }
        ```
        - About Rational neural networks:
        ```
        @inproceedings{boulle2020rational,
        title={Rational neural networks},
        author={Boull{\'e}, Nicolas and Nakatsukasa, Yuji and Townsend, Alex},
        booktitle = {Advances in Neural Information Processing Systems},
        volume = {33},
        pages = {14243--14253},
        year={2020},
        url = {https://proceedings.neurips.cc/paper/2020/file/a3f390d88e4c41f2747bfa2f1b5f87db-Paper.pdf}
        }
        ```
        
Keywords: Deep Learning,Machine Learning,Neural Networks,Scientific computing,Green's functions,PDE learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
