Advancing Reacting Flow Simulations with Data-Driven Models
This work addresses the challenge of integrating machine learning into multi-physics problems like combustion for researchers and engineers, but it is incremental as it reviews existing methods rather than introducing new ones.
The chapter reviews opportunities for applying data-driven reduced-order modeling to combustion systems, focusing on coupling machine learning with physical models to enhance performance by incorporating prior knowledge and constraints.
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, including combustion, is to couple them to physical and computer models. The performance of these tools is enhanced if all the prior knowledge and the physical constraints are embodied. In other words, the scientific method must be adapted to bring machine learning into the picture, and make the best use of the massive amount of data we have produced, thanks to the advances in numerical computing. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems. Examples of feature extraction in turbulent combustion data, empirical low-dimensional manifold (ELDM) identification, classification, regression, and reduced-order modeling are provided.