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About this paper
Rishikesh Ranade, Kevin Gitushi, Tarek Echekki
The authors train a DeepONet to estimate joint probability densities for turbulent-flame chemistry. They apply PCA to composition data, conditioning predictions on a local mean PC state and a queried state instead of constructing separate KDE estimates for each query. On flames D, E, and F, two-PC contours reasonably resemble KDE references, but some flame-F locations show errors.
Summary by alphaXiv.
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