5 citations · 9 across the 4 of their papers we have counts for
4 papers
Solver-in-the-loop training of deep learning closures for large-eddy simulation of turbulent premixed jet flames
Priyesh Kakka, Jonathan F. MacArt
Large-eddy simulation (LES) turbulence models often fail to capture the effects of chemical heat release and the resulting modulation of turbulence in premixed flames, underscoring…
Neural network-augmented eddy viscosity closures for turbulent premixed jet flames
Priyesh Kakka, Jonathan F. MacArt
Extending gradient-type turbulence closures to turbulent premixed flames is challenging due to the significant influence of combustion heat release. We incorporate a deep neural ne…
Sampling-based Distributed Training with Message Passing Neural Network
Priyesh Kakka, Sheel Nidhan, Rishikesh Ranade +2
In this study, we introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN). Our objective is to address the cha…
Sequence to sequence AE-ConvLSTM network for modelling the dynamics of PDE systems
Priyesh Rajesh Kakka
The article explains the convolutional LSTM (ConvLSTM) network in detail and introduces an improved auto-encoder version of the ConvLSTM network called AE-ConvLSTM. AE-ConvLSTM is…