Machine Learning for Combustion Chemistry

T. Echekki; A. Farooq; M. Ihme; S. M. Sarathy

doi:10.1007/978-3-031-16248-0_5

Machine Learning for Combustion Chemistry

T. Echekki^*, A. Farooq, M. Ihme, S. M. Sarathy

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

9 Scopus citations

Abstract

Machine learning provides a set of new tools for the analysis, reduction and acceleration of combustion chemistry. The implementation of such tools is not new. However, with the emerging techniques of deep learning, renewed interest in implementing machine learning is fast growing. In this chapter, we illustrate applications of machine learning in understanding chemistry, learning reaction rates and reaction mechanisms and in accelerating chemistry integration.

Original language	English (US)
Title of host publication	Lecture Notes in Energy
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	117-147
Number of pages	31
DOIs	https://doi.org/10.1007/978-3-031-16248-0_5
State	Published - 2023

Publication series

Name	Lecture Notes in Energy
Volume	44
ISSN (Print)	2195-1284
ISSN (Electronic)	2195-1292

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

ASJC Scopus subject areas

General Energy

Access to Document

10.1007/978-3-031-16248-0_5

Cite this

@inbook{20e63ad6d45043da9553c23b0de7dcae,

title = "Machine Learning for Combustion Chemistry",

abstract = "Machine learning provides a set of new tools for the analysis, reduction and acceleration of combustion chemistry. The implementation of such tools is not new. However, with the emerging techniques of deep learning, renewed interest in implementing machine learning is fast growing. In this chapter, we illustrate applications of machine learning in understanding chemistry, learning reaction rates and reaction mechanisms and in accelerating chemistry integration.",

author = "T. Echekki and A. Farooq and M. Ihme and Sarathy, {S. M.}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

doi = "10.1007/978-3-031-16248-0_5",

language = "English (US)",

series = "Lecture Notes in Energy",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "117--147",

booktitle = "Lecture Notes in Energy",

address = "Germany",

}

TY - CHAP

T1 - Machine Learning for Combustion Chemistry

AU - Echekki, T.

AU - Farooq, A.

AU - Ihme, M.

AU - Sarathy, S. M.

PY - 2023

Y1 - 2023

N2 - Machine learning provides a set of new tools for the analysis, reduction and acceleration of combustion chemistry. The implementation of such tools is not new. However, with the emerging techniques of deep learning, renewed interest in implementing machine learning is fast growing. In this chapter, we illustrate applications of machine learning in understanding chemistry, learning reaction rates and reaction mechanisms and in accelerating chemistry integration.

AB - Machine learning provides a set of new tools for the analysis, reduction and acceleration of combustion chemistry. The implementation of such tools is not new. However, with the emerging techniques of deep learning, renewed interest in implementing machine learning is fast growing. In this chapter, we illustrate applications of machine learning in understanding chemistry, learning reaction rates and reaction mechanisms and in accelerating chemistry integration.

UR - http://www.scopus.com/inward/record.url?scp=85146222616&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-16248-0_5

DO - 10.1007/978-3-031-16248-0_5

M3 - Chapter

AN - SCOPUS:85146222616

T3 - Lecture Notes in Energy

SP - 117

EP - 147

BT - Lecture Notes in Energy

PB - Springer Science and Business Media Deutschland GmbH

ER -