Machine Learning and Its Application to Reacting Flows ML and Combustion /

Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Swaminathan, Nedunchezhian (Editor), Parente, Alessandro (Editor)
Summary:	XI, 346 p. 127 illus., 98 illus. in color. text
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2023.
Edition:	1st ed. 2023.
Series:	Lecture Notes in Energy, 44
Subjects:	Cogeneration of electric power and heat. Fossil fuels. Thermodynamics. Heat engineering. Heat transfer. Mass transfer. Machine learning. Fossil Fuel. Engineering Thermodynamics, Heat and Mass Transfer. Machine Learning.
Online Access:	https://doi.org/10.1007/978-3-031-16248-0
Format:	Electronic Book

Description
Physical Description:	XI, 346 p. 127 illus., 98 illus. in color. online resource.
Summary:	XI, 346 p. 127 illus., 98 illus. in color. text
ISBN:	9783031162480
ISSN:	2195-1292 ;
DOI:	10.1007/978-3-031-16248-0
Access:	Open Access

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