AI RESEARCH
Solving physics-constrained inverse problems with conditional flow matching
arXiv CS.LG
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ArXi:2603.14135v1 Announce Type: cross This study presents a conditional flow matching framework for solving physics-constrained Bayesian inverse problems. In this setting, samples from the joint distribution of inferred variables and measurements are assumed available, while explicit evaluation of the prior and likelihood densities is not required. We derive a simple and self-contained formulation of both the unconditional and conditional flow matching algorithms, tailored specifically to inverse problems.