Search Results for author:
Found 4 papers, 1 papers with code
Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net
These models use convolutional neural networks to reduce data dimensions effectively.
A Finite Element-Inspired Hypergraph Neural Network: Application to Fluid Dynamics Simulations
We term this method a finite element-inspired hypergraph neural network, in short FEIH($\phi$)-GNN.
Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics
To alleviate these errors, we propose a novel technique for learning coupled spatial-temporal correlation using a 3D convolution network.
Predicting waves in fluids with deep neural network
The attention-based sequence-to-sequence network increases the time-horizon of prediction compared to the standard recurrent neural network with long short-term memory cells.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
