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 processes meet NeuralODEs: a Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data
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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.