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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Using a variance-matched Gaussian/Wiener diagonal estimator under skewed input incurs an excess L2 risk governed by the skew coefficient δ=μ3/σ², which vanishes exactly for symmetric inputs.
citing papers explorer
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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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A Closed-Form Skew Penalty for Volterra Cross-Correlation Identification under Non-Gaussian Input
Using a variance-matched Gaussian/Wiener diagonal estimator under skewed input incurs an excess L2 risk governed by the skew coefficient δ=μ3/σ², which vanishes exactly for symmetric inputs.