The recently developed physics-informed neural network (PINN) has achieved success in
many science and engineering disciplines by encoding physics laws into the loss functions …

Abstract Machine learning has long been considered a black box for predicting combustion
chemical kinetics due to the extremely large number of parameters and the lack of …

We leverage the computational singular perturbation (CSP) theory to develop an adaptive
time-integration scheme for stiff chemistry based on a local, projection-based, reduced order …

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 …

The application of artificial neural network (ANN) models in replacement of numerical
integration solvers for ordinary differential equations (ODE) has attracted increasing interest …

Fuel chemistry represents a typical complex system involving thousands of intermediate
species and elementary reactions. Traditional mechanism reduction methods, such as …

State-to-state numerical simulations of high-speed reacting flows are the most detailed but
also often prohibitively computationally expensive. In this work, we explore the usage of …

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 …

A novel chemistry reduction scheme incorporating neural networks and a pre-existing
reduction algorithm called Global Pathway Selection (GPS) is proposed and validated. GPS …

A novel coarse-grained analysis approach is proposed to represent detailed combustion
mechanism in wide operating conditions range. Based on the concepts from complex …