HMC
A standard algorithm in lattice field theory for generating equilibrium configurations. It evolves field configurations using Hamiltonian dynamics and employs a Metropolis accept-reject step to guarantee correct sampling.
Timeline (last 90 days)
This data-free training scheme achieves performance comparable to a diffusion model trained with HMC reference data in two-dimensional φ⁴ theory.
HMC belongs to the Markov chain Monte Carlo methods, and near the critical point the autocorrelation time diverges with the system volume (critical slowing down), causing sampling costs to rise sharply.
If data-free training can remain effective in higher dimensions and more complex gauge theories, researchers will no longer need to run expensive HMC for each parameter point to generate training sets, and can directly train samplers to generate independent configurations.
All Facts (3)
This data-free training scheme achieves performance comparable to a diffusion model trained with HMC reference data in two-dimensional φ⁴ theory.
50%UnverifiedHMC belongs to the Markov chain Monte Carlo methods, and near the critical point the autocorrelation time diverges with the system volume (critical slowing down), causing sampling costs to rise sharply.
50%UnverifiedIf data-free training can remain effective in higher dimensions and more complex gauge theories, researchers will no longer need to run expensive HMC for each parameter point to generate training sets, and can directly train samplers to generate independent configurations.
50%