The paper extends symplectic spectral Gaussian processes with energy, volume, and Lyapunov regularizers to learn conservative, dissipative, and port-Hamiltonian dynamics from noisy data.
A single-loop smoothed gradient descent-ascent algorithm for nonconvex-concave min-max problems
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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.