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This paper proposes a Bayesian data-driven machine learning method for theonline inference of the parameters of a G-equation model of a ducted, premixedflame. Heteroscedastic Bayesian neural network ensembles are trained on alibrary of 1.7 million flame fronts simulated in LSGEN2D, a G-equation solver,to learn the Bayesian posterior distribution of the model parameters givenobservations. The ensembles are then used to infer the parameters of Bunsenflame experiments so that the dynamics of these can be simulated in LSGEN2D.This allows the surface area variation of the flame edge, a proxy for the heatrelease rate, to be calculated. The proposed method provides cheap and onlineparameter and uncertainty estimates matching results obtained with the ensembleKalman filter, at less computational cost. This enables fast and reliablesimulation of the combustion process.

Online parameter inference for the simulation of a Bunsen flame using heteroscedastic Bayesian neural network ensembles