29 citations · 59 across the 17 of their papers we have counts for
4 papers · 1 filter
Modeling subgrid scale production rates on complex meshes using graph neural networks
Priyabrat Dash, Mathis Bode, Konduri Aditya
Large-eddy simulations (LES) require closures for filtered production rates because the resolved fields do not contain all correlations that govern chemical source terms. We develo…
Super-resolution of turbulent reacting flows on complex meshes using graph neural networks
Priyabrat Dash, Konduri Aditya, Christos E. Frouzakis +1
State-of-the-art deep learning models have been extensively utilized to reconstruct small-scale structures from coarse-grained data in turbulent flows. However, their application h…
A co-kurtosis PCA based dimensionality reduction with nonlinear reconstruction using neural networks
Dibyajyoti Nayak, Anirudh Jonnalagadda, Uma Balakrishnan +2
For turbulent reacting flows, identification of low-dimensional representations of the thermo-chemical state space is vitally important, primarily to significantly reduce the compu…
A co-kurtosis based dimensionality reduction method for combustion datasets
Anirudh Jonnalagadda, Shubham P. Kulkarni, Akash Rodhiya +2
Principal Component Analysis (PCA) is a dimensionality reduction technique widely used to reduce the computational cost associated with numerical simulations of combustion phenomen…