Iterated Energy-based Flow Matching for Sampling from Boltzmann Densities

Understand

In this work, we consider the problem of training a generator from evaluations of energy functions or unnormalized densities.

• This is a fundamental problem in probabilistic inference, which is crucial for scientific applications such as learning the 3D coordinate distribution of a molecule.
• To solve this problem, we propose iterated energy-based flow matching (iEFM), the first off-policy approach to train continuous normalizing flow (CNF) models from unnormalized densities.
• We introduce the simulation-free energy-based flow matching objective, which trains the model to predict the Monte Carlo estimation of the marginal vector field constructed from known energy functions.