Combining Flow Matching and Transformers for Efficient Solution of
  Bayesian Inverse Problems
Authors: name: Daniil Sherki, name: Ivan Oseledets, name: Ekaterina Muravleva

Solving Bayesian inverse problems efficiently remains a significant challenge
due to the complexity of posterior distributions and the computational cost of
traditional sampling methods. Given a series of observations and the forward
model, we want to recover the distribution of the parameters, conditioned on
observed experimental data. We show, that combining Conditional Flow Mathching
(CFM) with transformer-based architecture, we can efficiently sample from such
kind of distribution, conditioned on variable number of observations.