Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling
Learned dynamical proposals can generate configurations without providing a tractable endpoint density. Nonequilibrium path probabilities offer a way to correct such proposals, but correction alone does not determine their ability to connect separated regions. We introduce Neural Non-Equilibrium Hamiltonian Monte Carlo (NHMC), which combines conditional momentum distributions with reversible, volume preserving dynamics. The forward--reverse path ratio gives the work used for training, importance weighting, normalizer estimation, and Metropolis correction. We then construct a configuration space round-trip kernel whose reverse and forward paths share an intermediate configuration. Conditio...
| Authors | Moxian Qian |
|---|---|
| Primary category | cs.LG |
| Published | 17 July 2026 |
| Last updated at source | 12 October 2026 |
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