AI RESEARCH
Frequency-Separable Hamiltonian Neural Network for Multi-Timescale Dynamics
arXiv CS.LG
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ArXi:2603.06354v1 Announce Type: new While Hamiltonian mechanics provides a powerful inductive bias for neural networks modeling dynamical systems, Hamiltonian Neural Networks and their variants often fail to capture complex temporal dynamics spanning multiple timescales. This limitation is commonly linked to the spectral bias of deep neural networks, which favors learning low-frequency, slow-varying dynamics.