A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements
In this study, we develop a novel framework to extract turbulent combustion closure,
including closure for species chemical source terms, from multiscalar and velocity …
including closure for species chemical source terms, from multiscalar and velocity …
Physics-Informed Neural Networks for Turbulent Combustion: Toward Extracting More Statistics and Closure from Point Multiscalar Measurements
We develop a physics-informed neural network (PINN) to evaluate closure terms for
turbulence and chemical source terms in the Sandia turbulent nonpremixed flames. The …
turbulence and chemical source terms in the Sandia turbulent nonpremixed flames. The …
Deep Learning of Joint Scalar PDFs in Turbulent Flames from Sparse Multiscalar Data
We investigate the reconstruction of multi-dimensional joint scalar probability density
functions starting with sparse multiscalar data in turbulent flames using deep operator …
functions starting with sparse multiscalar data in turbulent flames using deep operator …