'Society for Industrial & Applied Mathematics (SIAM)'
Abstract
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 Mat\'ern 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 sub-models based on their heterogeneous functional complexity