An autoregressive Hamiltonian neural network coupled with an unscented Kalman filter improves long-term trajectory prediction and uncertainty quantification for unknown Hamiltonian systems.
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Learning Hamiltonian Dynamics with Bayesian Data Assimilation
An autoregressive Hamiltonian neural network coupled with an unscented Kalman filter improves long-term trajectory prediction and uncertainty quantification for unknown Hamiltonian systems.