Simulation-based optimal Bayesian experimental design for nonlinear systems

Xun Huan, Youssef M. Marzouk

Research output: Contribution to journalArticlepeer-review

164 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

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