Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes

Understand

Sampling configurations at thermodynamic equilibrium is a central challenge in statistical physics.

• Boltzmann Generators (BGs) tackle it by combining a generative model with a Monte Carlo (MC) correction step to obtain asymptotically unbiased samples from an unnormalized target.
• Most current BGs use classic MC mechanisms such as importance sampling, which both require tractable likelihoods from the backbone model and scale poorly in high-dimensional, multi-modal targets.
• We study BGs built on annealed Monte Carlo (aMC), which is designed to overcome these limitations by bridging a simple reference to the target through a sequence of intermediate densities.