A two-step probabilistic DeepONet learns uncertainty in a low-dimensional coefficient space and maps it to output fields, producing structured, non-diagonal predictive covariance with single-pass inference.
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Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields
A two-step probabilistic DeepONet learns uncertainty in a low-dimensional coefficient space and maps it to output fields, producing structured, non-diagonal predictive covariance with single-pass inference.