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This chapter describes the use of machine learning (ML) algorithms with the linear-eddy mixing (LEM) based tabulation for modeling of subgrid turbulence-chemistry interaction. The focus will be on the use of artificial neural network (ANN), particularly, supervised deep learning (DL) techniques within the finite-rate kinetics framework. We discuss the accuracy and efficiency aspects of two different strategies, where LEM based tabulation is used in both of them. While in the first approach, referred to as LANN-LES, the subgrid reaction-rate term is obtained efficiently using ANN in the conventional LEMLES framework, in the other approach referred to as TANN-LES, the filtered reaction rate terms are obtained using ANN. First, we assess the implications of the employed network architecture, and the associated hyperparameters, such as the amount of training and test data, epoch, optimizer, learning rate, sample size, etc. Afterward, the effectiveness of the two strategies is examined by comparing with conventional LES and LEMLES approaches by considering canonical premixed and non-premixed configurations. Finally, we describe the key challenges and future outlook of the use of ML based subgrid modelling within the finite-rate kinetics framework.

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