Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks
Machine learning of Hamiltonian dynamics has driven growing interest in Hamiltonian neural networks (HNNs), which encode Hamilton's equations of motion into the learning architecture. Despite this progress, it remains unknown whether such networks can predict dynamical regimes absent from their training data, in particular the broad chaotic sea that emerges beyond the observed parameter interval. We address this question using a parameter-aware random-feature Hamiltonian neural network (RF-HNN). Trained using data from only a small number of control-parameter values at which invariant tori dominate, the RF-HNN predicts autonomous long-time dynamics at unseen parameter values where mixed p...
| Authors | Jaesung Choi |
|---|---|
| Primary category | nlin.CD |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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