Titlebook: Machine Learning and Its Application to Reacting Flows; ML and Combustion Nedunchezhian Swaminathan,Alessandro Parente Book‘‘‘‘‘‘‘‘ 2023 Th

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,Machine Learning Techniques in?Reactive Atomistic Simulations,apter highlights key outstanding challenges, promising approaches, and potential future developments. While the chapter relies on reactive atomistic simulations to motivate models and methods, these are more generally applicable to other modeling paradigms for reactive flows.

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A Novel In Situ Machine Learning Framework for Intelligent Data Capture and Event Detection,nd identifying optimal space-time subregions for projection-based model reduction. Additionally, we demonstrate the scalability of our framework using a HPC combustion application on the Cori supercomputer at the National Energy Research Scientific Computing Center (NERSC).

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On the Use of Machine Learning for Subgrid Scale Filtered Density Function Modelling in Large Eddy merical Simulation (DNS) datasets employed to train the DNNs in each test case are described. The DNN performances are shown and compared to typical presumed probability density function (PDF) models. Finally, this chapter examines the advantages and caveats of the DNN-based approach.