Two hybrid Bayesian surrogate training approaches integrate simulation and real-world data via a weighting strategy independent of surrogate family, shown in synthetic and real case studies to improve accuracy and diagnose simulation issues.
Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion.Reliability Engineering & System Safety, 106: 179–190, 2012
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Constructs classical orthogonal VWK basis for Volterra estimation and proves order-2 excess risk penalty for Gaussian basis under input skew, with conditioning experiments and Lean proof.
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Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy
Two hybrid Bayesian surrogate training approaches integrate simulation and real-world data via a weighting strategy independent of surrogate family, shown in synthetic and real case studies to improve accuracy and diagnose simulation issues.
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Volterra--Wiener--Kunchenko Orthogonalization: From Wiener--Hermite to Distribution-Matched Volterra Bases
Constructs classical orthogonal VWK basis for Volterra estimation and proves order-2 excess risk penalty for Gaussian basis under input skew, with conditioning experiments and Lean proof.