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Found 3 papers, 0 papers with code
A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models
Additionally, the study delves into the investigation of optimized prompt templates for the purpose of efficient extraction of scientific literature.
Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows
The goal of this work is to show how the JSK-means algorithm -- without modifying the input dataset -- produces clusters that capture regions of dynamical similarity, in that the clusters are redistributed towards high-sensitivity regions in phase space and are described by similarity in the source terms of samples instead of the samples themselves.
Using machine learning to construct velocity fields from OH-PLIF images
Ultimately, this work shows the powerful ability of the CNN to decode the three-dimensional PIV fields from input OH-PLIF images, providing a potential groundwork for a very useful tool for experimental configurations in which accessibility of forms of simultaneous measurements are limited.
