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.

翻译：在科学实验成本高昂的时代，多保真度仿真为预测性科学计算提供了强有力的工具。尽管多保真度建模已有显著进展，但现有模型未能纳入多保真度模拟器的重要"联合"特性——即不同模拟器组件的精度由不同保真度参数控制。此类联合模拟器在复杂核物理和天体物理应用中广泛存在。为此，我们提出一种新的联合多保真度高斯过程(CONFIG)模型，该模型将这种联合结构嵌入一种新颖的非平稳协方差函数中。研究表明，所提出的CONFIG模型能够捕捉关于联合模拟器数值收敛的先验知识，从而实现对多保真度系统的高效仿真。通过一系列数值实验及两个应用案例——悬臂梁挠度仿真和夸克-胶子等离子体演化仿真（据理论推测，这种等离子体在大爆炸后不久充满了宇宙），我们证明了CONFIG模型相较于现有最先进模型具有更优的预测性能。