Facilitating Bayesian analysis of combustion kinetic models with artificial neural network
Abstract
Bayesian analysis provides a framework for the inverse uncertainty quantification (UQ) of combustion kinetic models. As the workhorse of the Bayesian approach, the Markov chain Monte Carlo (MCMC) methods, however, incur a substantial computational cost. In this work, a surrogate model is employed to improve the traditional MCMC algorithm. Specifically, the test errors of three typical surrogate models are compared, namely Polynomial Chaos...
Paper Details
Title
Facilitating Bayesian analysis of combustion kinetic models with artificial neural network
Published Date
Mar 1, 2020
Journal
Volume
213
Pages
87 - 97
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