Neural Non-Equilibrium Hamiltonian Monte Carlo for Corrected Boltzmann Sampling

Excerpt

arXiv:2607.15682v3 Announce Type: replace Abstract: 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 pat