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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Applications in Energy and Combustion Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666352X23000857 |