Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …

Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …

In the present work, artificial neural networks (ANN) technique combined with flamelet
generated manifolds (FGM) is proposed to mitigate the memory issue of FGM models. A set …

The simulation of turbulent flames fully resolving the smallest flow scales and the thinnest
reaction zones goes along with specific requirements, which are discussed from …

This study introduces the gradient boosted decision tree (GBDT) as a machine learning
approach to circumvent the need for a direct integration of the typically stiff system of …

This investigation outlines a data-assisted approach that employs random forest classifiers
for local and dynamic submodel assignment in turbulent-combustion simulations. This …

A machine learning algorithm, the deep neural network (DNN) 1, is trained using a
comprehensive direct numerical simulation (DNS) dataset to predict joint filtered density …

Turbulent combustion modeling often faces a trade-off between the so-called flamelet-like
models and PDF-like models. Flamelet-like models, are characterized by a choice of a …

Chemical kinetics mechanisms are essential for understanding, analyzing, and simulating
complex combustion phenomena. In this study, a neural ordinary differential equation …