This paper explores the recent advancements in enhancing Computational Fluid Dynamics
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …

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

Recent developments in deep learning have brought many inspirations for the scientific
computing community and it is perceived as a promising method in accelerating the …

Parametric optimization is a critical component in designing and prototyping combustion
systems. However, existing parametric optimization methods often suffer from either …

In this paper, a multiscale physics-informed neural network (MPINN) approach is proposed
based on the regular physics-informed neural network (PINN) for solving stiff chemical …

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 …

We propose a new modeling strategy to build efficient neural network representations of
chemical kinetics. Instead of fitting the logarithm of rates, we embed the hyperbolic sine …

We examine the state-of-the-art neural network (NN) approach and its flexible
implementations in combustion reaction model uncertainty quantification (UQ), optimization …

Understanding the dynamic combustion behavior of a solid propellant with reaction
mechanisms is essential for designing rocket engines and safely disposing of expired …

The integration of chemistry poses a major bottleneck in numerical combustion modelling,
as a significant amount of simulation time is consumed in the direct integration (DI) of …