Separable Hamiltonian Neural Networks
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Abstract (arXiv)
Hamiltonian neural networks (HNNs) are state-of-the-art models that regress the vector field of a dynamical system under the learning bias of Hamilton's equations. A recent observation is that embedding a bias regarding the additive separability of the Hamiltonian reduces the regression complexity and improves regression performance. We propose separable HNNs that embed additive separability within HNNs using observational, learning, and inductive biases. We show that the proposed models are more effective than the HNN at regressing the Hamiltonian and the vector field. Consequently, the proposed models predict the dynamics and conserve the total energy of the Hamiltonian system more accurately.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 42 references that are in the map. First posted on arXiv 3 Sep 2023.
Builds on
13 of 42 references.
References · 13
- 2019Hamiltonian Neural Networkscited by 712
- 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 548
- 2018Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equationscited by 235
- 2018Deep Neural Networks Motivated by Partial Differential Equationscited by 187
- 2019Symplectic ODE-Net: Learning Hamiltonian Dynamics with Controlcited by 184
- 2019Symplectic Recurrent Neural Networkscited by 150
- 2019Hamiltonian Generative Networkscited by 137
- 2021Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systemscited by 67
- 2021Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Mapscited by 41
- 2020Hamiltonian neural networks for solving equations of motioncited by 33
- 2022Deconstructing the Inductive Biases of Hamiltonian Neural Networkscited by 31
- 2022Learning Neural Hamiltonian Dynamics: A Methodological Overviewcited by 8
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