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Predictive Uncertainty for Neural CAE Surrogates

Paper title Predictive Uncertainty for Neural CAE Surrogates

Tangsali et al. (NVIDIA) · Aero & CFD · Structures & crash

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.

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