The paper extends symplectic spectral Gaussian processes with energy, volume, and Lyapunov regularizers to learn conservative, dissipative, and port-Hamiltonian dynamics from noisy data.
Gaussian process port-Hamiltonian systems: Bayesian learning with physics prior
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
The paper extends symplectic spectral Gaussian processes with energy, volume, and Lyapunov regularizers to learn conservative, dissipative, and port-Hamiltonian dynamics from noisy data.