Neural Network Representation for Ordinary Differential Equations

Author:

Golovkina AnnaORCID,Kozynchenko VladimirORCID

Publisher

Springer International Publishing

Reference19 articles.

1. Karniadakis, G.E., et al.: Physics-informed machine learning. Nat. Rev. Phys. 3(6), 422–440 (2021). https://doi.org/10.1038/s42254-021-00314-5

2. Lutter, M. et al.: Deep lagrangian networks: Using physics as model prior for deep learning. In: International conference on learning representations (2019)

3. Greydanus, S. et al.: Hamiltonian neural networks. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA (2019)

4. Han, C.-D., et al.: Adaptable hamiltonian neural networks. Phys. Rev. Res. 3, 2 (2021). https://doi.org/10.1103/physrevresearch.3.023156

5. Lusch, B., et al.: Deep learning for universal linear embeddings of nonlinear dynamics. Nat. Commun. 9, 1 (2018). https://doi.org/10.1038/s41467-018-07210-0

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1. Polynomial Neural Layers for Numerical Modeling of Dynamical Processes;Computational Science and Its Applications – ICCSA 2023 Workshops;2023

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