license: cc-by-sa-4.0
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
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.