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Found 6 papers, 2 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.
Predicting fluid-structure interaction with graph neural networks
The structural state is implicitly modeled by the movement of the mesh on the solid-fluid interface; hence it makes the proposed framework quasi-monolithic.
Assessment of convolutional recurrent autoencoder network for learning wave propagation
In this article, we present the convolutional autoencoder recurrent network (CRAN) as a data-driven model for learning wave propagation phenomena.
Deep convolutional neural network for shape optimization using level-set approach
We demonstrate our ROM-based shape optimization framework on a gradient-based three-dimensional shape optimization problem to minimize the induced drag of a wing in low-fidelity potential flow.
Kinematically consistent recurrent neural networks for learning inverse problems in wave propagation
The efficacy of the proposed method in alleviating overfitting, and the physical interpretability of the learning mechanism, are also discussed.
