Residual-augmented flow matching operators for probabilistic partial differential equations
A residual-augmented probabilistic operator learning framework that casts flow-matching-based generative modeling in infinite-dimensional function spaces while leveraging inexpensive low-fidelity solution operators as an inductive bias and enables accurate characterization of uncertainty in low-data regimes.
Sahil Bhola, Karthik Duraisamy
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