A physics informed emulator for laser-driven radiating shock simulations

Ryan G. McClarren, D. Ryu, R. Paul Drake, Michael Grosskopf, Derek Bingham, Chuan-Chih Chou, Bruce Fryxell, Bart van der Holst, James Paul Holloway, Carolyn C. Kuranz, Bani Mallick, Erica Rutter, Ben R. Torralva

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


This work discusses the uncertainty quantification aspect of quantification of margin and uncertainty (QMU) in the context of two linked computer codes. Specifically, we present a physics based reduction technique to deal with functional data from the first code and then develop an emulator for this reduced data. Our particular application deals with conditions created by laser deposition in a radiating shock experiment modeled using the Lagrangian, radiation-hydrodynamics code Hyades. Our goal is to construct an emulator and perform a sensitivity analysis of the functional output from Hyades to be used as an initial condition for a three-dimensional code that will compute the evolution of the radiating shock at later times. Initial attempts at purely statistical data reduction techniques, were not successful at reducing the number of parameters required to describe the Hyades output. We decided on an alternate approach using physical arguments to decide what features/locations of the output were relevant (e.g., the location of the shock front or the location of the maximum pressure) and then used a piecewise linear fit between these locations. This reduced the number of outputs needed from the emulator to 40, down from the O(1000) points in the Hyades output. Then, using Bayesian MARS and Gaussian process regression, we were able to build emulators for Hyades and study sensitivities to input parameters. © 2011 Elsevier Ltd. All rights reserved.
Original languageEnglish (US)
Pages (from-to)1194-1207
Number of pages14
JournalReliability Engineering & System Safety
Issue number9
StatePublished - Sep 2011
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research was supported by the DOE NNSA/ASC under the Predictive Science Academic Alliance Program by Grant number DEFC52- 08NA28616. R.G. McClarren, D. Ryu, and B. Mallick's contributions were partially supported by Award no. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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