Maximizing the differential entropy of a hierarchical Gaussian process posterior, with hyperparameter uncertainty propagated from MCMC samples, selects informative new data points for hybrid physical surrogates.
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BITS for GAPS: Bayesian Information-Theoretic Sampling for hierarchical GAussian Process Surrogates
Maximizing the differential entropy of a hierarchical Gaussian process posterior, with hyperparameter uncertainty propagated from MCMC samples, selects informative new data points for hybrid physical surrogates.