A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty
This work addresses adaptive sampling in multi-fidelity Gaussian processes for computationally demanding tasks like optimization and uncertainty quantification, representing an incremental improvement.
The authors tackled the problem of adaptive sampling for multi-fidelity Gaussian processes by partitioning prediction uncertainty based on fidelity level and cost, and using a Believer concept to quantify the effect of new design points. They demonstrated the framework on academic examples and an industrial fluidized bed process application.
Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification. Adaptive sampling for multi-fidelity Gaussian process is a changing task due to the fact that not only we seek to estimate the next sampling location of the design variable, but also the level of the simulator fidelity. This issue is often addressed by including the cost of the simulator as an another factor in the searching criterion in conjunction with the uncertainty reduction metric. In this work, we extent the traditional design of experiment framework for the multi-fidelity Gaussian process by partitioning the prediction uncertainty based on the fidelity level and the associated cost of execution. In addition, we utilize the concept of Believer which quantifies the effect of adding an exploratory design point on the Gaussian process uncertainty prediction. We demonstrated our framework using academic examples as well as a industrial application of steady-state thermodynamic operation point of a fluidized bed process