Simulation-based optimal Bayesian experimental design for nonlinear systems

Xun Huan, Youssef M. Marzouk

Research output: Contribution to journalArticlepeer-review

290 Scopus citations

Abstract

The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general mathematical framework and an algorithmic approach for optimal experimental design with nonlinear simulation-based models; in particular, we focus on finding sets of experiments that provide the most information about targeted sets of parameters.Our framework employs a Bayesian statistical setting, which provides a foundation for inference from noisy, indirect, and incomplete data, and a natural mechanism for incorporating heterogeneous sources of information. An objective function is constructed from information theoretic measures, reflecting expected information gain from proposed combinations of experiments. Polynomial chaos approximations and a two-stage Monte Carlo sampling method are used to evaluate the expected information gain. Stochastic approximation algorithms are then used to make optimization feasible in computationally intensive and high-dimensional settings. These algorithms are demonstrated on model problems and on nonlinear parameter inference problems arising in detailed combustion kinetics. © 2012 Elsevier Inc.
Original languageEnglish (US)
Pages (from-to)288-317
Number of pages30
JournalJournal of Computational Physics
Volume232
Issue number1
DOIs
StatePublished - Jan 2013
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The authors would like to acknowledge support from the KAUST Global Research Partnership and from the US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) under Grant No. DE-SC0003908.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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