36 citations · 69 across the 11 of their papers we have counts for
Showing 2022Show all
3 papers · 1 filter
physics.flu-dyn2022★ 32 cited
A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
Rui Gao, Indu Kant Deo, Rajeev K. Jaiman
An emerging trend in deep learning research focuses on the applications of graph neural networks (GNNs) for mesh-based continuum mechanics simulations. Most of these learning frame…
physics.flu-dyn2022
Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics
Indu Kant Deo, Rui Gao, Rajeev Jaiman
There is a critical need for efficient and reliable active flow control strategies to reduce drag and noise in aerospace and marine engineering applications. While traditional full…
physics.flu-dyn2022★ 36 cited
Predicting waves in fluids with deep neural network
Indu Kant Deo, Rajeev Jaiman
In this paper, we present a deep learning technique for data-driven predictions of wave propagation in a fluid medium. The technique relies on an attention-based convolutional recu…