Deep learning dynamical latencies for the analysis and reduction of combustion chemistry kinetics

Castellanos, Luisa;S. M. Freitas, Rodolfo;Parente, Alessandro;Contino, Francesco
(2023) Physics of Fluids — Vol. 35, n° 10, p. 13 (2023)

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The modeling of chemical kinetics holds many challenges, as well as a necessity for more efficient modeling techniques, together with dimensionality reduction techniques. This work studies the application of time-lag auto-encoders for the analysis of combustion chemistry kinetics. Such a technique allows a better reconstruction of the thermochemical temporal advancement in relation to traditional reduction techniques (principal component analysis) while applying a potential denoising operation. Moreover, the reduced manifolds or latencies are provided with physical meaning, which further analysis gives insight into key chemical reactions and interactions between chemical species, allowing for a deeper understanding of the chemical mechanism itself.
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Castellanos, L., S. M. Freitas, R., Parente, A., & Contino, F. (2023). Deep learning dynamical latencies for the analysis and reduction of combustion chemistry kinetics. Physics of Fluids, 35(10), 13. https://doi.org/10.1063/5.0167110 (Original work published 2023)