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 (modeling separate physics) 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：在科学实验常常成本高昂的时代，多保真度仿真为预测性科学计算提供了强大的工具。尽管已有大量关于多保真度建模的工作，但现有模型并未纳入多保真度模拟器的一个重要“聚合”特性，即不同模拟器组件（分别建模不同物理过程）的精度受不同保真度参数控制。此类聚合模拟器在复杂的核物理和天体物理应用中广泛出现。为此，我们提出了一种新的聚合多保真度高斯过程（CONFIG）模型，该模型通过一种新颖的非平稳协方差函数嵌入这种聚合结构。我们证明，所提出的CONFIG模型能够捕捉关于聚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成本高效仿真。通过一系列数值实验以及两个应用（第一个用于模拟悬臂梁挠度，第二个用于模拟夸克-胶子等离子体的演化——该等离子体被认为在大爆炸后不久充满了宇宙），我们展示了CONFIG相较于当前最优模型的预测性能提升。