Combining Flow Matching and Transformers for Efficient Solution of
  Bayesian Inverse Problems
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.
67c6bdf644c2f41804ca95c6
Daniil Sherki
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2025-03-04T09:44:52.446Z
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Daniil Sherki
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dsherki
67c6bdf644c2f41804ca95c7
Ivan Oseledets
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2025-03-07T09:46:17.295Z
6169a581d05945bfd8718dfa
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Ivan Oseledets
user
oseledets
67c6bdf644c2f41804ca95c8
Ekaterina Muravleva
Combining Flow Matching and Transformers for Efficient Solution of
  Bayesian Inverse Problems
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.
Conditional Flow Matching combined with a transformer-based architecture efficiently samples posterior distributions in Bayesian inverse problems, accommodating a variable number of observations.
Conditional Flow Matching transformer-based architecture Bayesian inverse problems posterior distributions