Phy-ChemNODE: an end-to-end physics-constrained autoencoder-NeuralODE framework for learning stiff chemical kinetics of hydrocarbon fuels
Predictive computational fluid dynamics (CFD) simulations of reacting flows in energy conversion systems are accompanied by a major computational bottleneck of solving a stiff system of coupled ordinary differential equations (ODEs) associated with detailed fuel chemistry. This issue is exacerbated...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Thermal Engineering |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fther.2025.1594443/full |
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