Advancing Reacting Flow Simulations with Data-Driven Models (Prof. Alessandro Parente) – Part 3

This lecture was given by Prof. Alessandro Parente, Université Libre de Bruxelles, Belgium in the framework of the von Karman Lecture Series on Machine Learning for Fluid Mechanics organized by the von Karman Institute and the Université libre de Bruxelles in February 2020. The use of machine learning algorithms to predict the behaviors of complex systems is booming. However, the key to an effective use of machine learning tools in multi-physics problems, including combustion, is to couple them to physical and computer models, to embody in them all the prior knowledge and physical constraints that can enhance their performances, and to improve them based on the feedback coming for the validation experiments. In other words, we need to adapt the scientific method to bring machine learning into the picture and make the best use of the massive amount of data we have produced thanks to the advances in numerical computing. The present talk reviews some of the open opportunities for the application of data-driven, reduced-order modelling of combustion systems. Examples of feature extraction in turbulent combustion data, empirical low dimensional manifold identification, classification, regression and reduced order modelling will be provided.