Combustion machine learning: Principles, progress and prospects

Progress in combustion science and engineering has led to the generation of large amounts of data from largescale simulations, high-resolution experiments, and sensors. This corpus of data offers enormous opportunities for extracting new knowledge and insights-if harnessed effectively. Machine learning (ML) techniques have demonstrated remarkable success in data analytics, thus offering a new paradigm for data-intense analyses and scientific investigations through combustion machine learning (CombML). While data-driven methods are utilized in various combustion areas, recent advances in algorithmic developments, the accessibility of open-source software libraries, the availability of computational resources, and the abundance of data have together rendered ML techniques ubiquitous in scientific analysis and engineering. This article examines ML techniques for applications in combustion science and engineering. Starting with a review of sources of data, data-driven techniques, and concepts, we examine supervised, unsupervised, and semi-supervised ML methods. Various combustion examples are considered to illustrate and to evaluate these methods. Next, we review past and recent applications of ML approaches to problems in combustion, spanning fundamental combustion investigations, propulsion and energy-conversion systems, and fire and explosion hazards. Challenges unique to CombML are discussed and further opportunities are identified, focusing on interpretability, uncertainty quantification, robustness, consistency, creation and curation of benchmark data, and the augmentation of ML methods with prior combustion-domain knowledge.

Automated summary

This article discusses the importance and challenges of machine learning techniques in combustion science and engineering. It introduces the sources of data and data-driven techniques, and provides a detailed review of supervised, unsupervised, and semi-supervised machine learning methods. Through case studies and evaluations, the article demonstrates the wide applications of machine learning in combustion. In looking towards the future, it identifies issues such as interpretability, uncertainty quantification, and robustness, and suggests further research opportunities.