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2022-02-16 14:05:24 +01:00
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* [Dependencies and installation](#dependencies-and-installation)
* [Installing via PIP](#installing-via-pip)
* [Installing from source](#installing-from-source)
* [Documentation](#documentation)
* [Testing](#testing)
<!-- * [Documentation](#documentation) -->
<!-- * [Testing](#testing) -->
* [Examples and Tutorials](#examples-and-tutorials)
* [Awards](#awards)
<!-- * [Awards](#awards) -->
* [How to cite](#how-to-cite)
* [References](#references)
* [Recent works with PyDMD](#recent-works-with-pydmd)
<!-- * [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)
@@ -103,7 +103,10 @@ The directory `Examples` contains some examples showing Poisson and Burgers prob
### 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)].
* Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis.
*Physics-informed neural networks: A deep learning framework for solving
forward and inverse problems involving nonlinear partial differential
equations.* Journal of Computational Physics 378 (2019): 686-707.
## Authors and contributors