Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design
Deyu Ming, Serge Guillas · SIAM/ASA Journal on Uncertainty Quantification · 2021
The state-of-the-art linked Gaussian process offers a way to build analytical emulators for systems of computer models. We generalize the closed form expressions for the linked Gaussian process under the squared exponential kernel to a class of Matérn kernels that are essential in advanced applications. An iterative procedure to construct linked Gaussian processes as surrogate models for any feed-forward systems of computer models is presented and illustrated on a feed-back coupled satellite system. We also introduce an adaptive design algorithm that could increase the approximation accuracy of linked Gaussian process surrogates with reduced computational costs on running expensive computer systems, by allocating runs and refining emulators of individual submodels based on their heterogeneous functional complexity.