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.
Sudret, Global sensitivity analysis using polynomial chaos expan- sions, Reliability Engineering & System Safety 93 (7) (2008) 964–979
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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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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.