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A purely data-driven approach using deep convolutional neural networks isdiscussed in the context of Large Eddy Simulation (LES) of turbulent premixedflames. The assessment of the method is conducted a priori using directnumerical simulation data. The network has been trained to performdeconvolution on the filtered density and the filtered density- progressvariable product, and by doing so obtain estimates of the un-filtered progressvariable field. Any non-linear function of the progress variable can then beapproximated on the LES coarse mesh and explicitly filtered to advance the LESsolution in time. This new strategy for tackling turbulent combustion modellingis demonstrated with success, for both the sub-grid scale progress variablevariance and the flamelet filtered reaction rate, two fundamental ingredientsof premixed turbulent combustion modelling.

Modelling turbulent premixed flames using convolutional neural networks: application to sub-grid scale variance and filtered reaction rate