Physics-constrained machine learning for reduced composition space chemical kinetics

Modeling detailed chemical kinetics is a primary challenge in combustion simulations. We present a novel framework to enforce physical constraints, specifically total mass and elemental conservation, during the reaction of ML models’ training for the reduced composition space chemical kinetics of la...

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Hlavní autoři:	Anuj Kumar, Tarek Echekki
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Cambridge University Press 2025-01-01
Edice:	Data-Centric Engineering
Témata:	chemistry acceleration deep operator networks physics-informed machine learning reduced order modeling surrogate modeling
On-line přístup:	https://www.cambridge.org/core/product/identifier/S2632673625100129/type/journal_article
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