Fea ext lagrangian MeshGraphNet

[zongyi-li]

Modulus Pull Request for Lagrangian MeshGraphNet

Description

We implemented Meshgraphnet for particle-based simulation on the water dataset based on
https://github.com/google-deepmind/deepmind-research/tree/master/learning_to_simulate in PyTorch.
It demonstrates how to train a Graph Neural Network (GNN) for evaluation of the
Lagrangian fluid.

In this project, we provide an example of Lagrangian mesh simulation for fluids. The Lagrangian mesh is particle-based, where vertices represent fluid particles and edges represent their interactions. Compared to an Eulerian mesh, where the mesh grid is fixed, a Lagrangian mesh is more flexible since it does not require tessellating the domain or aligning with boundaries.

As a result, Lagrangian meshes are well-suited for representing complex geometries and free-boundary problems, such as water splashes and object collisions. However, a drawback of the Lagrangian mesh is that it typically requires smaller time steps to maintain physically valid simulations.

The main code consists of dataloader, train, and inference

• modulus/datapipes/gnn/lagrangian_dataset.py
• examples/cfd/lagrangian_mgn/train.py
• examples/cfd/lagrangian_mgn/inference.py

Checklist

• I am familiar with the Contributing Guidelines.
• New or existing tests cover these changes.
• The documentation is up to date with these changes.
• The CHANGELOG.md is up to date with these changes.
• An issue is linked to this pull request.

Dependencies

• tensorflow to load the datasets

[michaltakac]

This is really great! Was thinking that it would make sense to also have an example for lagrangian GNN based on DeepMind's paper... Great work!

[zongyi-li]

Thanks for the comments! I have just addressed them and added a unit test test/datapipes/test_lagrangian.py. Please take a look!

[mnabian]

/blossom-ci

[mnabian]

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[mnabian]

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[mnabian]

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[mnabian]

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[zongyi-li]

/blossom-ci

[mnabian]

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[mnabian]

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[mnabian]

LGTM