NeuralGraphPDE

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.