  In an era where scientific experimentation is often costly, multi-fidelity
emulation provides a powerful tool for predictive scientific computing. While
there has been notable work on multi-fidelity modeling, existing models do not
incorporate an important "conglomerate" property of multi-fidelity simulators,
where the accuracies of different simulator components are controlled by
different fidelity parameters. Such conglomerate simulators are widely
encountered in complex nuclear physics and astrophysics applications. We thus
propose a new CONglomerate multi-FIdelity Gaussian process (CONFIG) model,
which embeds this conglomerate structure within a novel non-stationary
covariance function. We show that the proposed CONFIG model can capture prior
knowledge on the numerical convergence of conglomerate simulators, which allows
for cost-efficient emulation of multi-fidelity systems. We demonstrate the
improved predictive performance of CONFIG over state-of-the-art models in a
suite of numerical experiments and two applications, the first for emulation of
cantilever beam deflection and the second for emulating the evolution of the
quark-gluon plasma, which was theorized to have filled the Universe shortly
after the Big Bang.
