Machine learning for combustion system control and simulation

Real-world combustion systems are highly complex with scales that span many orders of magnitude making them particularly challenging for numerical simulations. Two large challenges are the numerical simulation and control of these systems in engineering applications under realistic conditions. Machine learning has emerged over the past 5+ years to show immense promise in data science applications and also in many real-world engineering applications. Here, we discuss the recent application of various machine learning techniques to assist in the development of control strategies for compression ignition engines using deep reinforcement learning and also the approximation of chemical kinetics mechanisms using supervised learning for applications in computational fluid dynamics codes.