3 papers
cs.LG2026
A graph neural network based chemical mechanism reduction method for combustion applications
Manuru Nithin Padiyar, Priyabrat Dash, Konduri Aditya
Direct numerical simulations of turbulent reacting flows involving millions of grid points and detailed chemical mechanisms with hundreds of species and thousands of reactions are…
physics.flu-dyn2026
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…
physics.flu-dyn2026
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…