Add project descriptioin

README.md

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-license: cc-by-sa-4.0
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+license: cc-by-sa-4.0
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+This dataset corresponds to a tutorial to learn how to use MeshNet, a model for simulating fluid flows. 
+The MeshNet model is publicly available as open source in Github "Graph Network Simulator (GNS) and MeshNet" (See Referenced Data and Software). 
+GNS is a generalizable, efficient, and accurate machine learning (ML)-based surrogate simulator that uses Graph Neural Networks (GNNs). 
+GNS is a viable surrogate for numerical methods such as Material Point Method, Smooth Particle Hydrodynamics and Computational Fluid dynamics and can be extended to simulate natural hazards. 
+GNS  can handle complex boundary conditions and multi-material interactions. GNS exploits distributed data parallelism to achieve fast multi-GPU training. 
+The dataset in this publication includes training/testing/validating partitions to make GNS learn and simulate fluid flow through a cylinder obstacle. 
+It was created using Computational Fluid Dynamics originally from DeepMind. A link to the dataset is available in Related Data and Software and a contextual paper about the MeshNet is available in Related Works.
+The data was transformed to `.npz` format for compatibility with the Pytroch-based multi-GPU parallel version of GNS code. 
+Detailed instructions on how to use this data are published along with this version of the dataset and also located as a `README.md` file in the GitHub repository. 
+The documentation lives with the model because instructions may change as the model is versioned in the GitHub repository. 
+[https://github.com/geoelements/gns](https://github.com/geoelements/gns)
+The main function of this dataset is for purposes of training new users. 
+Once users know how to run the model they can use other kinds of datasets to train and simulate different natural hazards.