title: Combining Flow Matching and Transformers for Efficient Solution of
    Bayesian Inverse Problems
summary: 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.
paper_authors._id: 67c6bdf644c2f41804ca95c6
paper_authors.name: Daniil Sherki
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-04T09:44:52.446Z
paper_authors._id: 64e57772b15cf1b5d017b8ee
paper_authors.avatarUrl: /avatars/24653ae6259a706d9d4ed63692eac5b7.svg
paper_authors.fullname: Daniil Sherki
paper_authors.type: user
paper_authors.user: dsherki
paper_authors._id: 67c6bdf644c2f41804ca95c7
paper_authors.name: Ivan Oseledets
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-07T09:46:17.295Z
paper_authors._id: 6169a581d05945bfd8718dfa
paper_authors.avatarUrl: /avatars/1892ab06a7ddb557232777de3cbec470.svg
paper_authors.fullname: Ivan Oseledets
paper_authors.type: user
paper_authors.user: oseledets
paper_authors._id: 67c6bdf644c2f41804ca95c8
paper_authors.name: Ekaterina Muravleva
paper_title: Combining Flow Matching and Transformers for Efficient Solution of
    Bayesian Inverse Problems
paper_summary: 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.
paper_ai_summary: Conditional Flow Matching combined with a transformer-based architecture efficiently samples posterior distributions in Bayesian inverse problems, accommodating a variable number of observations.
paper_ai_keywords: Conditional Flow Matching transformer-based architecture Bayesian inverse problems posterior distributions