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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. |
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