Data-driven simulation of ammonia combustion using neural ordinary differential equations (NODE)

Data-driven simulation of ammonia combustion using neural ordinary differential equations (NODE)

The direct use of detailed chemical kinetics in combustion simulations is limited by the extremely high computational costs. Recently, Owoyele and Pal (Energy and AI, 2022), proposed the neural ordinary differential equations (NODE) method to accelerate calculations of chemical kinetics and proved i...

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Bibliographic Details
Main Authors: Manabu Saito, Jiangkuan Xing, Jun Nagao, Ryoichi Kurose
Format: Article
Language:English
Published: Elsevier 2023-12-01
Series:Applications in Energy and Combustion Science
Subjects:
Chemical kinetics
Machine learning
Neural ordinary differential equations
Online Access:http://www.sciencedirect.com/science/article/pii/S2666352X23000857

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http://www.sciencedirect.com/science/article/pii/S2666352X23000857

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