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 …

The present work advances the PC-transport approach in the context of Large Eddy
Simulation (LES) of turbulent combustion. Accurate modeling of combustion systems …

Reduced-order models (ROMs) for turbulent combustion rely on identifying a small number
of parameters that can effectively describe the complexity of reacting flows. With the advent …

The selection of an appropriate combustion model for the numerical prediction of reacting
flows remains an outstanding issue. Often, expert knowledge or experimental data is …

In this work, we demonstrate a framework for developing closure models in turbulent
combustion using experimental multi-scalar measurements. The framework is based on the …

This paper presents a posteriori validation of the solution of a turbulent combustion problem
based on the transport of principal components (PCs). The PCs are derived from a priori …

Kernel principal component analysis (KPCA) as a nonlinear alternative to classical principal
component analysis (PCA) of combustion composition space is investigated. With the …

Many scientific disciplines rely on dimensionality reduction techniques for computationally
less expensive handling of multivariate data sets. In particular, Principal Component …

Effective dimension reduction is a key factor in facilitating large-scale simulation of high-
dimensional dynamical systems. The behaviour of low-dimensional surrogate models often …