A data-based reduced-order model (ROM) is developed to accelerate the time integration of
stiff chemically reacting systems by effectively removing the stiffness arising from a wide …

For their ability to account for finite-rate chemistry, reactor-based models are well suited
Turbulence–Chemistry Interactions (TCI) Sub-Grid Scale (SGS) closures for Large Eddy …

With the increase in computational power in the last decade and the forthcoming Exascale
supercomputers, a new horizon in computational modelling and simulation is envisioned in …

PyCSP is a Python package for the analysis and simplification of chemically reacting
systems, using algorithms based on the Computational Singular Perturbation (CSP) theory …

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 …

Stiff systems of ordinary differential equations (ODEs) are pervasive in many science and
engineering fields, yet standard neural ODE approaches struggle to learn them. This …

We present a physics-informed machine learning (PIML) approach for the approximation of
slow invariant manifolds of singularly perturbed systems, providing functionals in an explicit …

The modeling of chemical kinetics holds many challenges, as well as a necessity for more
efficient modeling techniques, together with dimensionality reduction techniques. This work …

Abstract Machine learning provides a set of new tools for the analysis, reduction and
acceleration of combustion chemistry. The implementation of such tools is not new …

Detailed chemical kinetics calculations can be very computationally expensive, and so
various approaches have been used to speed up combustion calculations. Deep neural …