1. Luz, I., Galun, M., Maron, H., Basri, R., Yavneh, I.: Learning algebraic multigrid using graph neural networks. In: International Conference on Machine Learning, pp. 6489–6499. PMLR (2020)
2. Jiang, Z., Jiang, J., Yao, Q., et al.: A neural network-based PDE solving algorithm with high precision. Sci. Rep. 13, 4479 (2023). https://doi.org/10.1038/s41598-023-31236-0
3. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems, vol. 32, pp. 8024–8035. Curran Associates, Inc. (2019)
4. Li, S. et al.: PyTorch distributed: experiences on accelerating data parallel training, In: VLDB Endowment, vol. 13, no. 12 (2020). https://doi.org/10.14778/3415478.3415530
5. Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019)