Calibrating a large computer experiment simulating radiative shock hydrodynamics

TL;DR

This paper develops a scalable calibration method for large computer experiments simulating radiative shock hydrodynamics, combining local Gaussian process regression, modularization, and adaptive search to overcome computational challenges.

Contribution

It introduces a novel, efficient calibration approach that handles large-scale simulations by integrating modern emulation, optimization, and modular techniques.

Findings

Effective calibration of large radiative shock models demonstrated

Insights into shock dynamics from real-data application

Limitations of current calibration methods highlighted

Abstract

We consider adapting a canonical computer model calibration apparatus, involving coupled Gaussian process (GP) emulators, to a computer experiment simulating radiative shock hydrodynamics that is orders of magnitude larger than what can typically be accommodated. The conventional approach calls for thousands of large matrix inverses to evaluate the likelihood in an MCMC scheme. Our approach replaces that costly ideal with a thrifty take on essential ingredients, synergizing three modern ideas in emulation, calibration and optimization: local approximate GP regression, modularization, and mesh adaptive direct search. The new methodology is motivated both by necessity - considering our particular application - and by recent trends in the supercomputer simulation literature. A synthetic data application allows us to explore the merits of several variations in a controlled environment and, together with results on our motivating real-data experiment, lead to noteworthy insights into the dynamics of radiative shocks as well as the limitations of the calibration enterprise generally.