Derives error bounds on the root prior-preconditioned Hessian, posterior covariance, and mean for a Petrov-Galerkin reduced-order model, with exact posterior recovery at the intrinsic dimension.
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A multivariate active learning approach for polynomial chaos expansion selects samples by aggregated output variance to improve surrogate accuracy and stability for vector-valued engineering responses.
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Error bounds for approximate posteriors from likelihood-informed reduced-order models
Derives error bounds on the root prior-preconditioned Hessian, posterior covariance, and mean for a Petrov-Galerkin reduced-order model, with exact posterior recovery at the intrinsic dimension.
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Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning
A multivariate active learning approach for polynomial chaos expansion selects samples by aggregated output variance to improve surrogate accuracy and stability for vector-valued engineering responses.