1. Ayed, I., de Bézenac, E., Pajot, A., Brajard, J., Gallinari, P.: Learning dynamical systems from partial observations. CoRR abs/1902.11136 (2019). http://arxiv.org/abs/1902.11136
2. Belbute-Peres, F.D.A., Economon, T., Kolter, Z.: Combining differentiable PDE solvers and graph neural networks for fluid flow prediction. In: International Conference on Machine Learning, pp. 2402–2411. PMLR, November 2020. https://proceedings.mlr.press/v119/de-avila-belbute-peres20a.html
3. Berg, J., Nyström, K.: Data-driven discovery of PDEs in complex datasets. J. Comput. Phys. 384, 239–252 (2019). https://doi.org/10.1016/j.jcp.2019.01.036, http://arxiv.org/abs/1808.10788, arXiv: 1808.10788
4. Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond Euclidean data. IEEE Sig. Process. Mag. 34(4), 18–42 (2017). https://doi.org/10.1109/MSP.2017.2693418, http://arxiv.org/abs/1611.08097, arXiv: 1611.08097 version: 1
5. Chen, R.T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.K.: Neural ordinary differential equations. In: Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018). https://papers.nips.cc/paper/2018/hash/69386f6bb1dfed68692a24c8686939b9-Abstract.html