@Article{CiCP-28-1812, author = {Deng , YixiangLin , 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 = {

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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2020-0151}, url = {http://global-sci.org/intro/article_detail/cicp/18397.html} }