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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Autori principali:	Tadbhagya Kumar, Anuj Kumar, Pinaki Pal
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Frontiers Media S.A. 2025-08-01
Serie:	Frontiers in Thermal Engineering
Soggetti:	stiff chemical kinetics fuel combustion autoencoders neural ordinary differential equations physics-informed neural network chemistry acceleration
Accesso online:	https://www.frontiersin.org/articles/10.3389/fther.2025.1594443/full
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