This repository provides sample applications demonstrating use of specific Physics-ML model architectures that are easy to train and deploy. These examples aim to show how such models can help solve real world problems.
Use case | Model | Transient | Parameterized |
---|---|---|---|
Vortex Shedding | MeshGraphNet | YES | YES |
Navier-Stokes Flow | RNN | YES | NO |
Gray-Scott System | RNN | YES | NO |
Darcy Flow | FNO | NO | YES |
Darcy Flow | Nested-FNO | NO | YES |
Use case | Model | AMP | CUDA Graphs | Multi-GPU | Multi-Node |
---|---|---|---|---|---|
Medium-range global weather forecast | FCN-SFNO | YES | NO | YES | YES |
Medium-range global weather forecast | GraphCast | YES | NO | YES | YES |
Medium-range global weather forecast | FCN-AFNO | YES | YES | YES | YES |
Medium-range and S2S global weather forecast | DLWP | YES | YES | YES | YES |
In each of the example READMEs, we indicate the level of support that will be provided. Some examples are under active development/improvement and might involve rapid changes. For stable examples, please refer the tagged versions.
We're posting these examples on GitHub to better support the community, facilitate feedback, as well as collect and implement contributions using GitHub issues and pull requests. We welcome all contributions!