Accelerating Combustion Simulations with Machine Learning Surrogate Models

Abstract

This dissertation explores the use of machine learning surrogate models to accelerate combustion simulations. By leveraging advanced techniques, the study demonstrates how computational efficiency can be significantly improved without compromising accuracy. The work focuses on developing and validating surrogate models for turbulent combustion flows, providing insights into their practical applications in engineering and research.