Forecasting Hamiltonian dynamics without canonical coordinates
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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
Estimating the Number of Significant Canonical Coordinates
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2933255
Abstract: This study proposes a novel model selection criterion for dimensionality estimation in canonical correlation analysis (CCA), which can be used to estimate the number of correlated components between two sets of multivariate vectors, particularly in… read more here.
Keywords: number; fit term; goodness fit; canonical coordinates ... See more keywords