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-# PINA
-# Physic Informed Neural networks for Advance modeling
+<!-- <p align="center"> -->
+<!--   <a href="http://mathlab.github.io/PyDMD/" target="_blank" > -->
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+
+
+**PINA**: Physics-Informed Neural networks for Advance modeling
+
+## Table of contents
+* [Description](#description)
+* [Dependencies and installation](#dependencies-and-installation)
+	* [Installing via PIP](#installing-via-pip)
+	* [Installing from source](#installing-from-source)
+* [Documentation](#documentation)
+* [Testing](#testing)
+* [Examples and Tutorials](#examples-and-tutorials)
+* [Awards](#awards)
+* [How to cite](#how-to-cite)
+	* [References](#references)
+	* [Recent works with PyDMD](#recent-works-with-pydmd)
+* [Authors and contributors](#authors-and-contributors)
+* [How to contribute](#how-to-contribute)
+	* [Submitting a patch](#submitting-a-patch)
+* [License](#license)
+
+## Description
+**PINA** is a Python package providing an easy interface to deal with
+*physics-informed neural networks* (PINN) for the approximation of (differential,
+nonlinear, ...) functions. Based on Pytorch, PINA offers a simple and intuitive
+way to formalize a specific problem and solve it using PINN.
+
+
+## Dependencies and installation
+**PINA** requires requires `numpy`, `scipy`, `matplotlib`, `future`, `torch`, `sphinx` (for the documentation) and `pytest` (for local test). The code is tested for Python 3, while compatibility of Python 2 is not guaranteed anymore. It can be installed using `pip` or directly from the source code.
+
+### Installing via PIP
+Mac and Linux users can install pre-built binary packages using pip.
+To install the package just type:
+```bash
+> pip install git+https://github.com/mathLab/PINA.git
+```
+To uninstall the package:
+```bash
+> pip uninstall pina
+```
+
+### Installing from source
+The official distribution is on GitHub, and you can clone the repository using
+```bash
+> git clone https://github.com/mathLab/PINA
+```
+
+To install the package just type:
+```bash
+> pip install -e .
+```
+
+<!-- ## Documentation -->
+<!-- **PyDMD** uses [Sphinx](http://www.sphinx-doc.org/en/stable/) for code documentation. You can view the documentation online [here](http://mathlab.github.io/PyDMD/). To build the html version of the docs locally simply: -->
+
+<!-- ```bash -->
+<!-- > cd docs -->
+<!-- > make html -->
+<!-- ``` -->
+
+<!-- The generated html can be found in `docs/build/html`. Open up the `index.html` you find there to browse. -->
+
+
+<!-- ## Testing -->
+
+<!-- We are using Travis CI for continuous intergration testing. You can check out the current status [here](https://travis-ci.org/mathLab/PyDMD). -->
+
+<!-- To run tests locally (`pytest` is required): -->
+
+<!-- ```bash -->
+<!-- > pytest -->
+<!-- ``` -->
+
+## Examples and Tutorials
+The directory `Examples` contains some examples showing Poisson and Burgers problems solved in the PINN context.
+
+### References
+To implement the package we follow these works:
+
+* Kutz, Brunton, Brunton, Proctor. *Dynamic Mode Decomposition: Data-Driven Modeling of Complex Systems*. SIAM Other Titles in Applied Mathematics, 2016. [[DOI](https://doi.org/10.1137/1.9781611974508)] [[bibitem](readme/Kutz2016_1.bib)].
+
+
+## Authors and contributors
+**PINA** is currently developed and mantained at [SISSA mathLab](http://mathlab.sissa.it/) by
+* [Nicola Demo](mailto:demo.nicola@gmail.com)
+* [Maria Strazzullo](mailto:mstrazzu@gmail.com)
+
+
+under the supervision of [Prof. Gianluigi Rozza](mailto:gianluigi.rozza@sissa.it).
+
+Contact us by email for further information or questions about **PyDMD**, or suggest pull requests. Contributions improving either the code or the documentation are welcome!
+
+
+## How to contribute
+We'd love to accept your patches and contributions to this project. There are just a few small guidelines you need to follow.
+
+### Submitting a patch
+
+  1. It's generally best to start by opening a new issue describing the bug or
+     feature you're intending to fix.  Even if you think it's relatively minor,
+     it's helpful to know what people are working on.  Mention in the initial
+     issue that you are planning to work on that bug or feature so that it can
+     be assigned to you.
+
+  2. Follow the normal process of [forking][] the project, and setup a new
+     branch to work in.  It's important that each group of changes be done in
+     separate branches in order to ensure that a pull request only includes the
+     commits related to that bug or feature.
+
+  3. To ensure properly formatted code, please make sure to use 4
+     spaces to indent the code. The easy way is to run on your bash the provided
+     script: ./code_formatter.sh. You should also run [pylint][] over your code.
+     It's not strictly necessary that your code be completely "lint-free",
+     but this will help you find common style issues.
+
+  4. Any significant changes should almost always be accompanied by tests.  The
+     project already has good test coverage, so look at some of the existing
+     tests if you're unsure how to go about it. We're using [coveralls][] that
+     is an invaluable tools for seeing which parts of your code aren't being
+     exercised by your tests.
+
+  5. Do your best to have [well-formed commit messages][] for each change.
+     This provides consistency throughout the project, and ensures that commit
+     messages are able to be formatted properly by various git tools.
+
+  6. Finally, push the commits to your fork and submit a [pull request][]. Please,
+     remember to rebase properly in order to maintain a clean, linear git history.
+
+[forking]: https://help.github.com/articles/fork-a-repo
+[pylint]: https://www.pylint.org/
+[coveralls]: https://coveralls.io
+[well-formed commit messages]: http://tbaggery.com/2008/04/19/a-note-about-git-commit-messages.html
+[pull request]: https://help.github.com/articles/creating-a-pull-request
+
+
+## License
+
+See the [LICENSE](LICENSE.rst) file for license rights and limitations (MIT).