Patched Flow Matching reconstructs full-resolution wall-pressure fields on domains four times larger than training data from 0.25% sensor coverage by fusing short-domain DNS patch priors with sparse measurements via training-free posterior sampling.
arXiv preprint arXiv:2602.21469 , year=
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Physics-constrained generative sampling for PDE inverse problems omits a co-area Jacobian factor required for correct posterior sampling; CoCoS corrects it to match the true distribution.
Conditional flow matching learns a velocity field to sample from measurement-conditioned posteriors in physics inverse problems, with early stopping to prevent variance collapse and selective memorization under finite training data.
citing papers explorer
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Patched Flow Matching: Generative Wall-Pressure Reconstruction Beyond Training-Domain Scales from Sparse Sensors
Patched Flow Matching reconstructs full-resolution wall-pressure fields on domains four times larger than training data from 0.25% sensor coverage by fusing short-domain DNS patch priors with sparse measurements via training-free posterior sampling.
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The Right Measure for Physics-Constrained Generation: A Co-Area Correction for Posterior-Consistent PDE Inverse Problems
Physics-constrained generative sampling for PDE inverse problems omits a co-area Jacobian factor required for correct posterior sampling; CoCoS corrects it to match the true distribution.
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Conditional flow matching for physics-constrained inverse problems with finite training data
Conditional flow matching learns a velocity field to sample from measurement-conditioned posteriors in physics inverse problems, with early stopping to prevent variance collapse and selective memorization under finite training data.