CAE-UQ
Predictive Uncertainty for Neural CAE Surrogates
Paper title Predictive Uncertainty for Neural CAE Surrogates
Paper arXiv 2609.25430PDF
Fits three uncertainty methods to the same GeoTransolver surrogate: a sparse variational Gaussian-process output head with a learned residual variance, concrete Monte Carlo dropout, and a five-member deep ensemble. It tests them at three scopes: whether a whole new case can be trusted, where in the field the error lies, and how uncertain a derived quantity such as drag or intrusion is.
- Data
- AirfRANS
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