A modified Adam optimizer that works directly on the Stiefel manifold makes symplectic autoencoder training for Hamiltonian model reduction faster per update and, in most tested settings, more accurate than the homogeneous-space Adam baseline.
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Structure-preserving model reduction of Hamiltonian systems by learning a symplectic autoencoder
A modified Adam optimizer that works directly on the Stiefel manifold makes symplectic autoencoder training for Hamiltonian model reduction faster per update and, in most tested settings, more accurate than the homogeneous-space Adam baseline.