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Machine Learning and Its Application to Reacting Flows

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Machine Learning and Its Application to Reacting Flows
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Machine Learning Techniques in Reactive Atomistic Simulations
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    Chapter 3 A Novel In Situ Machine Learning Framework for Intelligent Data Capture and Event Detection
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    Chapter 4 Machine-Learning for Stress Tensor Modelling in Large Eddy Simulation
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    Chapter 5 Machine Learning for Combustion Chemistry
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    Chapter 6 Deep Convolutional Neural Networks for Subgrid-Scale Flame Wrinkling Modeling
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    Chapter 7 Machine Learning Strategy for Subgrid Modeling of Turbulent Combustion Using Linear Eddy Mixing Based Tabulation
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    Chapter 8 On the Use of Machine Learning for Subgrid Scale Filtered Density Function Modelling in Large Eddy Simulations of Combustion Systems
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    Chapter 9 Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches
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    Chapter 10 AI Super-Resolution: Application to Turbulence and Combustion
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    Chapter 11 Machine Learning for Thermoacoustics
Attention for Chapter 9: Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches
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