Accelerated Bayesian Experimental Design for Chemical Kinetic Models
Year: 2010
Type: dissertation
Abstract: The optimal selection of experimental conditions isessential in maximizing the value of data for inference and
prediction, particularly in situations where experiments are
time-consuming and expensive to conduct. A general Bayesian
framework for optimal experimental design with nonlinear
simulation-based models is proposed. The formulation accounts for
uncertainty in model parameters, observables, and experimental
conditions. Straightforward Monte Carlo evaluation of the objective
function - which reflects expected information gain
(Kullback-Leibler divergence) from prior to posterior - is
intractable when the likelihood is computationally intensive.
Instead, polynomial chaos expansions are introduced to capture the
dependence of observables on model parameters and on design
conditions. Under suitable regularity conditions, these expansions
converge exponentially fast. Since both the parameter space and the
design space can be high-dimensional, dimension-adaptive sparse
quadrature is used to construct the polynomial expansions.
Stochastic optimization methods will be used in the future to
maximize the expected utility. While this approach is broadly
applicable, it is demonstrated on a chemical kinetic system with
strong nonlinearities. In particular, the Arrhenius rate parameters
in a combustion reaction mechanism are estimated from observations
of autoignition. Results show multiple order-of-magnitude speedups
in both experimental design and parameter
inference.
Author: Xun Huan
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1579213377