29 citations · 59 across the 17 of their papers we have counts for
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Scalability of the asynchronous discontinuous Galerkin method for compressible flow simulations
Shubham Kumar Goswami, Dapse Vidyesh, Konduri Aditya
The scalability of time-dependent partial differential equation (PDE) solvers based on the discontinuous Galerkin (DG) method is increasingly limited by data communication and sync…
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…
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…
ChemXDyn: Dynamics-informed species and reaction detection methodology from atomistic simulations
Raj Maddipati, Dhruthi Boddapati, Elangannan Arunan +2
Accurate identification of chemical species and reaction pathways from molecular dynamics (MD) trajectories is a prerequisite for deriving predictive chemical-kinetic models and fo…