Conglomerate Multi-Fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions

TL;DR

This paper introduces CONFIG, a Gaussian process model that captures the unique conglomerate structure of multi-fidelity simulators, improving predictive accuracy in complex scientific applications like nuclear physics.

Contribution

The paper proposes a novel non-stationary covariance function for Gaussian processes that models conglomerate multi-fidelity simulators, enhancing emulation efficiency and accuracy.

Findings

CONFIG outperforms existing models in numerical experiments.

It effectively captures prior knowledge on simulator convergence.

Demonstrated in applications to beam deflection and quark-gluon plasma evolution.

Abstract

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.