search

YichengDWu / NeuralGraphPDE.jl

Integrating Neural Ordinary Differential Equations, the Method of Lines, and Graph Neural Networks
https://yichengdwu.github.io/NeuralGraphPDE.jl/dev/
MIT License
18 stars 1 forks source link
deep-learning equivariant-representations gnn graph-neural-networks julia partial-differential-equations

readme

NeuralGraphPDE

Stable Dev Build Status Coverage SciML Code Style

This package is based on GraphNeuralNetwork.jl and Lux.jl.

The goal is to extend Neural (Graph) ODE to Neural Graph PDE (experimental).

Technically, it has become a general framework for graph neural networks.

References

  1. Iakovlev V, Heinonen M, Lähdesmäki H. Learning continuous-time PDEs from sparse data with graph neural networks[J]. arXiv preprint arXiv:2006.08956, 2020.
  2. Poli M, Massaroli S, Rabideau C M, et al. Continuous-depth neural models for dynamic graph prediction[J]. arXiv preprint arXiv:2106.11581, 2021.
  3. Chamberlain B, Rowbottom J, Gorinova M I, et al. Grand: Graph neural diffusion[C]. International Conference on Machine Learning. PMLR, 2021: 1407-1418.
  4. Brandstetter J, Worrall D, Welling M. Message passing neural PDE solvers[J]. arXiv preprint arXiv:2202.03376, 2022.
  5. Li Z, Kovachki N, Azizzadenesheli K, et al. Neural operator: Graph kernel network for partial differential equations[J]. arXiv preprint arXiv:2003.03485, 2020.
  6. Toshev, Artur, et al. "On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methods." ICML 2022 2nd AI for Science Workshop. 2022.

Current Status

This package is no longer actively maintained.

What does this mean?

Can I still use the package?

Yes, you can continue to use the package, but please do so with caution. As it won’t be updated, it may not work in future versions of dependent technologies.