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Multi-fidelity Gaussian process is a common approach to address the extensivecomputationally demanding algorithms such as optimization, calibration anduncertainty quantification. Adaptive sampling for multi-fidelity Gaussianprocess is a changing task due to the fact that not only we seek to estimatethe next sampling location of the design variable, but also the level of thesimulator fidelity. This issue is often addressed by including the cost of thesimulator as an another factor in the searching criterion in conjunction withthe uncertainty reduction metric. In this work, we extent the traditionaldesign of experiment framework for the multi-fidelity Gaussian process bypartitioning the prediction uncertainty based on the fidelity level and theassociated cost of execution. In addition, we utilize the concept of Believerwhich quantifies the effect of adding an exploratory design point on theGaussian process uncertainty prediction. We demonstrated our framework usingacademic examples as well as a industrial application of steady-statethermodynamic operation point of a fluidized bed process

A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty