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 probabilistic framework combining robust regression, Sobol indices, Monte Carlo propagation, and AIC/BIC model selection for uncertainty-aware creep remaining useful life prediction.
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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 framework for probabilistic prediction of remaining useful life in structural materials
A probabilistic framework combining robust regression, Sobol indices, Monte Carlo propagation, and AIC/BIC model selection for uncertainty-aware creep remaining useful life prediction.