Forecasting Hamiltonian dynamics without canonical coordinates
Sign Up to like & getrecommendations! Published in 2020 at "Nonlinear Dynamics"
DOI: 10.1007/s11071-020-06185-2
Abstract: Conventional neural networks are universal function approximators, but they may need impractically many training data to approximate nonlinear dynamics. Recently introduced Hamiltonian neural networks can efficiently learn and forecast dynamical systems that conserve energy, but… read more here.
Keywords: hamiltonian dynamics; without canonical; dynamics without; pendulum ... See more keywords