@Article{CiCP-28-5,
author = {Yixiang, Deng and Lin, Guang and Yang, Xiu},
title = {Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression},
journal = {Communications in Computational Physics},
year = {2020},
volume = {28},
number = {5},
pages = {1812--1837},
abstract = {<p style="text-align: justify;">We propose a data fusion method based on multi-fidelity Gaussian process
regression (GPR) framework. This method combines available data of the quantity of
interest (QoI) and its gradients with different fidelity levels, namely, it is a Gradient-enhanced Cokriging method (GE-Cokriging). It provides the approximations of both
the QoI and its gradients simultaneously with uncertainty estimates. We compare this
method with the conventional multi-fidelity Cokriging method that does not use gradients information, and the result suggests that GE-Cokriging has a better performance
in predicting both QoI and its gradients. Moreover, GE-Cokriging even shows better
generalization result in some cases where Cokriging performs poorly due to the singularity of the covariance matrix. We demonstrate the application of GE-Cokriging
in several practical cases including reconstructing the trajectories and velocity of an
underdamped oscillator with respect to time simultaneously, and investigating the
sensitivity of power factor of a load bus with respect to varying power inputs of a
generator bus in a large scale power system. Although GE-Cokriging requires slightly
higher computational cost than Cokriging in some cases, the comparison of the accuracy shows that this cost is worthwhile.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2020-0151},
url = {https://global-sci.com/article/79741/multifidelity-data-fusion-via-gradient-enhanced-gaussian-process-regression}
}