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 use of machine learning algorithms to predict behaviors of complex systems is booming.
However, the key to an effective use of machine learning tools in multi-physics problems …

We investigate the implementation of principal component (PC) transport to accelerate the
direct numerical simulation (DNS) of turbulent combustion flows. The acceleration is …

In reduced-order modeling, complex systems that exhibit high state-space dimensionality
are described and evolved using a small number of parameters. These parameters can be …

We describe an update to our open-source Python package, PCAfold, designed to help
researchers generate, analyze and improve low-dimensional data manifolds. In the current …

A combustion chemistry acceleration scheme for implementation in reacting flow simulations
is developed based on deep operator nets (DeepONets). The scheme is based on a …

Detailed chemistry computations are indispensable in numerous complex simulation tasks,
which focus on accurately capturing the ignition process or predicting pollutant levels. The …

Principal component transport-based data-driven reduced-order models (PC-transport
ROM) are being increasingly adopted as a combustion model of turbulent reactive flows to …

In many reacting flow systems, the thermo-chemical state-space is known or assumed to
evolve close to a low-dimensional manifold (LDM). Various approaches are available to …

For turbulent reacting flow systems, identification of low-dimensional representations of the
thermo-chemical state space is vitally important, primarily to significantly reduce the …