Chapter 1 Introduction

Chapter 2 Machine Learning Techniques in Reactive Atomistic Simulations

Chapter 3 A Novel In Situ Machine Learning Framework for Intelligent Data Capture and Event Detection

Chapter 4 Machine-Learning for Stress Tensor Modelling in Large Eddy Simulation

Chapter 5 Machine Learning for Combustion Chemistry

Chapter 6 Deep Convolutional Neural Networks for Subgrid-Scale Flame Wrinkling Modeling

Chapter 7 Machine Learning Strategy for Subgrid Modeling of Turbulent Combustion Using Linear Eddy Mixing Based Tabulation

Chapter 8 On the Use of Machine Learning for Subgrid Scale Filtered Density Function Modelling in Large Eddy Simulations of Combustion Systems

Chapter 9 Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches

Chapter 10 AI Super-Resolution: Application to Turbulence and Combustion

Chapter 11 Machine Learning for Thermoacoustics