A reduced-order Hamiltonian neural network (RO-HNN) learns a symplectic low-dimensional embedding and the dynamics on it, enabling stable long-term prediction for high-dimensional Hamiltonian systems up to 600 DoF.
Symplectic model reduction of H amiltonian systems on nonlinear manifolds and approximation with weakly symplectic autoencoder
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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach
A reduced-order Hamiltonian neural network (RO-HNN) learns a symplectic low-dimensional embedding and the dynamics on it, enabling stable long-term prediction for high-dimensional Hamiltonian systems up to 600 DoF.