Many problems in science and engineering can be represented by a set of partial differential
equations (PDEs) through mathematical modeling. Mechanism-based computation following …

Due to the significant improvement in computing power and the rapid advancement of data
processing technologies, artificial intelligence (AI) has introduced new tools and …

Deep operator networks (DeepONets) are powerful and flexible architectures that are
attracting attention in multiple fields due to their utility for fast and accurate emulation of …

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 …

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 …

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 …

Many state-of-the-art machine learning (ML) fields rely on large datasets and massive deep
learning models (with O (10 9) trainable parameters) to predict target variables accurately …

In this study, we develop a novel framework to extract turbulent combustion closure,
including closure for species chemical source terms, from multiscalar and velocity …

Abstract Principal Component Analysis can be used to reduce the cost of Computational
Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the …

D soot temperature and volume fraction reconstruction of afterburner flame has been based
on spectrally resolved measurement methods and inverse problem theory. So far, the …