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Combustion Machine Learning: Principles, Progress, and Perspectives

AMS Seminar Series

Speaker: Matthias Ihme, Stanford University

Presentation

Seminar Slide Deck (PDF-37MB)

Abstract

Progress in combustion science and engineering has led to the generation of large amounts of data from large-scale 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, thereby 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 presentation examines ML techniques for applications in combustion science and engineering. Starting with a review of sources of data, data-driven techniques, and concepts, we discuss supervised, unsupervised, and semi-supervised ML methods. We proceed by reviewing 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, the need for community-driven benchmark data, and the augmentation of ML methods with prior combustion-domain knowledge.

Biography

Photo of Matthias Ihme
Matthias Ihme is Professor in the Department of Mechanical Engineering and the Department of Photon Sciences at Stanford University. He holds a BSc. degree in Mechanical Engineering and a MSc. degree in Computational Engineering. In 2008, he received his Ph.D. in Mechanical Engineering from Stanford. After being on the faculty of the Aerospace Engineering Department at the University of Michigan for five years, he returned to Stanford in 2013. He is a recipient of the NSF CAREER Award (2009), the ONR Young Investigator Award (2010), the AFOSR Young Investigator Award (2010), the NASA Early Career Faculty Award (2015), the Hiroshi Tsuji Early Career Research Award (2017), and the Bessel Award of Alexander von Humboldt Foundation (2021). His research interests are broadly on the computational modeling of reacting flows, the development of numerical methods, and the investigation of advanced energy conversion concepts and molecular processes.