Optimal Bayesian Experimental Design for Priors of Compact Support with Application to Shock‐Tube Experiments for Combustion Kinetics

The analysis of reactive systems in combustion science and technology relies on detailed models comprising many chemical reactions that describe the conversion of fuel and oxidizer into products and the formation of pollutants. Shock‐tube experiments are a convenient setting for measuring the rate parameters of individual reactions. The temperature, pressure, and concentration of reactants are chosen to maximize the sensitivity of the measured quantities to the rate parameter of the target reaction. In this study, we optimize the experimental setup computationally by optimal experimental design in a Bayesian framework. We approximate the posterior probability density functions (pdf) using truncated Gaussian distributions in order to account for the bounded domain of the uniform prior pdf of the parameters. The underlying Gaussian distribution is obtained in the spirit of the Laplace method, more precisely, the mode is chosen as the maximum a posteriori (MAP) estimate, and the covariance is chosen as the negative inverse of the Hessian of the misfit function at the MAP estimate. The model related entities are obtained from a polynomial surrogate. The optimality, quantified by the information gain measures, can be estimated efficiently by a rejection sampling algorithm against the underlying Gaussian probability distribution, rather than against the true posterior. This approach offers a significant error reduction when the magnitude of the invariants of the posterior covariance are comparable with the size of the bounded domain of the prior. We demonstrate the accuracy and superior computational efficiency of our method for shock‐tube experiments aiming to measure the model parameters of a key reaction, which is part of the complex kinetic network describing the hydrocarbon oxidation. In the experiments, the initial temperature and fuel concentration are optimized with respect to the expected information gain in the estimation of the parameters of the target reaction rate. We show that the expected information gain surface can change its “shape” dramatically according to the level of noise introduced into the synthetic data. The information that can be extracted from the data saturates as a logarithmic function of the number of experiments, and few experiments are needed when they are conducted at the optimal experimental design conditions. Furthermore, inversion of the legacy data indicates the validity and robustness of our designs. Copyright © 2016 John Wiley & Sons, Ltd.     

A comparative study of expected improvement-assisted global optimization with different surrogates

Efficient global optimization (EGO) uses the surrogate uncertainty estimator called expected improvement (EI) to guide the selection of the next sampling candidates. Theoretically, any modelling methods can be integrated with the EI criterion. To improve the convergence ratio, a multi-surrogate efficient global optimization (MSEGO) was suggested. In practice, the EI-based optimization methods with different surrogates show widely divergent characteristics. Therefore, it is important to choose the most suitable algorithm for a certain problem. For this purpose, four single-surrogate efficient global optimizations (SSEGOs) and an MSEGO involving four surrogates are investigated. According to numerical tests, both the SSEGOs and the MSEGO are feasible for weak nonlinear problems. However, they are not robust for strong nonlinear problems, especially for multimodal and high-dimensional problems. Moreover, to investigate the feasibility of EGO in practice, a material identification benchmark is designed to demonstrate the performance of EGO methods. According to the tests in this study, the kriging EGO is generally the most robust method.