An iterative data-consistent inversion procedure converges to a measure satisfying multiple push-forward constraints, minimizing cumulative f-divergence and yielding the maximum-entropy solution under uniform initialization.
and Mikelsons, L., Solving Stochastic Inverse Problems with Stochastic BayesFlow, In2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp
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Iterative Data-Consistent Inversion with Multiple Push-forward Constraints
An iterative data-consistent inversion procedure converges to a measure satisfying multiple push-forward constraints, minimizing cumulative f-divergence and yielding the maximum-entropy solution under uniform initialization.