A non‐stationary covariance‐based Kriging method for metamodelling in engineering design

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Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (2015, methodology): “, by introducing non-stationary Gaussian processes to model the discrepancy [6], using multiplicative discrepancy term [7], or using high-fidelity models and field measurements to provide observation data [7–9].”
Composite Gaussian process models for emulating expensive functions (2012, methodology): “Consider a simple example from Xiong et al. (2007).”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “To this end, one way is to introduce some non-stationary models, e.g., the Bayesian treed GP model (Gramacy and Lee 2009) and the non-stationary covariance-based Kriging model (Xiong et al. 2007), and then apply the variance based sampling criteria to them (Gramacy and Lee 2006, 2009).”
Recent advances in surrogate-based optimization (2009, background): “It is possible to build a surrogate with a non-stationary covariance [86].”