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The Quark-Gluon Plasma (QGP) is a unique phase of nuclear matter, theorizedto have filled the Universe shortly after the Big Bang. A critical challenge instudying the QGP is that, to reconcile experimental observables withtheoretical parameters, one requires many simulation runs of a complex physicsmodel over a high-dimensional parameter space. Each run is computationally veryexpensive, requiring thousands of CPU hours, thus limiting physicists to onlyseveral hundred runs. Given limited training data for high-dimensionalprediction, existing surrogate models often yield poor predictions with highpredictive uncertainties, leading to imprecise scientific findings. To addressthis, we propose a new Additive Multi-Index Gaussian process (AdMIn-GP) model,which leverages a flexible additive structure on low-dimensional embeddings ofthe parameter space. This is guided by prior scientific knowledge that the QGPis dominated by multiple distinct physical phenomena (i.e., multiphysics), eachinvolving a small number of latent parameters. The AdMIn-GP models for suchembedded structures within a flexible Bayesian nonparametric framework, whichfacilitates efficient model fitting via a carefully constructed variationalinference approach with inducing points. We show the effectiveness of theAdMIn-GP via a suite of numerical experiments and our QGP application, where wedemonstrate considerably improved surrogate modeling performance over existingmodels.

Additive Multi-Index Gaussian process modeling, with application to multi-physics surrogate modeling of the quark-gluon plasma