What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field
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Abstract (arXiv)
We present several methods for predicting the dynamics of Hamiltonian systems from discrete observations of their vector field. Each method is either informed or uninformed of the Hamiltonian property. We empirically and comparatively evaluate the methods and observe that information that the system is Hamiltonian can be effectively informed, and that different methods strike different trade-offs between efficiency and effectiveness for different dynamical systems.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 35 references that are in the map. First posted on arXiv 14 Dec 2023.
Builds on
9 of 35 references.
References · 9
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2019Hamiltonian Neural Networkscited by 702
- 2020Universal Differential Equations for Scientific Machine Learningcited by 345
- 2018Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equationscited by 233
- 2018Deep Neural Networks Motivated by Partial Differential Equationscited by 187
- 2019Symplectic Recurrent Neural Networkscited by 148
- 2021Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Mapscited by 41
- 2021Symplectic Learning for Hamiltonian Neural Networkscited by 35
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Citing papers per year: 2023: 1.
1 paper.